System and method for physiological feature derivation

ABSTRACT

The present disclosure relates to a device, method and system for calculating, estimating, or monitoring the blood pressure of a subject based on physiological features and personalized models. At least one processor, when executing instructions, may perform one or more of the following operations. A first signal representing a pulse wave relating to heart activity of a subject may be received. A plurality of second signals representing time-varying information on a pulse wave of the subject may be received. A personalized model for the subject may be designated. Effective physiological features of the subject based on the plurality of second signals may be determined. A blood pressure of the subject based on the effective physiological features and the designated model for the subject may be calculated.

CROSS REFERENCE TO RELATED APPLICATIONS

The application is a U.S. national stage under 35 U.S.C. § 371 ofInternational Application No. PCT/CN2017/076702, filed on Mar. 15, 2017,which claims priority to International Application No. PCT/CN2016/077469filed Mar. 28, 2016, each of which is hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to a personalized system andmethod applicable in health-care related areas. More particularly, thepresent disclosure relates to a system and method for physiologicalfeature derivation and blood pressure monitoring.

BACKGROUND

A traditional blood pressure measurement system, also calledsphygmomanometers, employs Korotkoff sounds or an oscillometric methodto determine blood pressure based on the relationship of the externalpressure and magnitude of arterial volume pulsations. In recent yearstechniques have been developed using pulse wave signals, obtained fromphotoelectric sensors placed on the finger of a subject, to derivephysiological features and estimate blood pressure. A system utilizingsuch techniques can be portable and monitor the blood pressure of asubject continuously. Continuous monitoring of multiple physiologicalfeatures may be beneficial for, for example, hypertension management andcardiovascular risk prediction.

SUMMARY

In a first aspect of the present disclosure, a device is provided. Thedevice includes memory storing instructions and at least one processorthat executes the instructions to perform operations comprising:receiving a first signal representing a pulse wave relating to heartactivity of a subject; receiving a plurality of second signalsrepresenting time-varying information on a pulse wave of the subject;designating a personalized model for the subject; determining effectivephysiological features of the subject based on the plurality of secondsignals; and calculating a blood pressure of the subject based on theeffective physiological features and the designated model for thesubject.

In the device provided above, the receiving the plurality of secondsignals comprises communicating with one or more second sensors.

Further, in the device provided above, the first sensor comprises aplurality of electrodes, and one of the one or more second sensorscomprises a photoelectric sensor.

Further, in the device provided above, the first signal or the secondsignal comprises an optical signal or an electric signal.

Further, in the device provided above, the effective physiologicalfeatures are obtained based on Akaike information criterion (AIC).

Further, in the device provided above, the first signal or the secondsignal comprises an ECG waveform, a PPG waveform, or a BCG waveform.

Further, the device provided above further comprises or is configured tocommunicate with a cuff-based blood pressure monitor.

Further, in the device provided above, the cuff-based blood pressuremonitor being configured to coordinate a blood pressure measurement withthe receiving of the first signal or the receiving of the plurality ofsecond signals.

In a second aspect of the present disclosure, a method is provided. Themethod includes: receiving a first signal representing a pulse waverelating to heart activity of a subject; receiving a plurality of secondsignals representing time-varying information on a pulse wave of thesubject; designating a personalized model for the subject; determiningeffective physiological features of the subject based on the pluralityof second signals; and calculating a blood pressure of the subject basedon the effective physiological features and the designated model for thesubject.

Further, the method provided above further comprises acquiring the firstsignal at a first location on the body of the subject.

Further, the method provided above further comprises acquiring thesecond signal at a second location on the body of the subject.

Further, in the method provided above, the first signal or at least oneof the plurality of second signals comprises an optical signal or anelectric signal.

Further, in the method provided above, the effective physiologicalfeatures are obtained based on Akaike information criterion (AIC).

Further, in the method provided above, the first signal or the secondsignal is acquired in real time or at a first time interval.

Further, in the method provided above, the set of calibration data isacquired at a second time interval.

In a third aspect of the present disclosure, a system is provided. Thesystem includes a first acquisition module configured to receive a firstsignal representing heart activity of a subject; a second acquisitionmodule configured to receive a plurality of second signals representingtime-varying information on the pulse wave; a calibration unitconfigured to acquire a set of calibration data; an analysis moduleconfigured to designate a personalized model for the subject, determineeffective physiological features of the subject based on the pluralityof second signals, and calculate a blood pressure of the subject basedon the effective physiological features and the designated model for thesubject.

Further, in the system provided above, the first acquisition modulecomprises an ECG monitor.

Further, in the system provided above, the second acquisition modulecomprises a blood oxygen monitor.

Further, in the system provided above, the first signal or one of theplurality of second signals comprises an optical signal or an electricsignal.

Further, in the system provided above, the calibration unit comprises oris configured to communicate with a cuff-based blood pressure monitor.

Further, in the system provided above, the cuff-based blood pressuremonitor is configured to coordinate a blood pressure measurement withthe first signal or the plurality of second signals.

Further, the system provided above further comprises an output moduleconfigured to provide the calculated blood pressure for output.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 illustrates an exemplary system configuration in which a systemfor monitoring a physiological signal may be deployed in accordance withvarious embodiments of the present disclosure;

FIG. 2 depicts an exemplary diagram of an engine of the systemillustrated in FIG. 1, according to some embodiments of the presentdisclosure;

FIG. 3 is a flowchart of an exemplary process in which a method forestimating a physiological signal is deployed, according to someembodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an architecture of an informationacquisition module according to some embodiments of the presentdisclosure;

FIG. 5 is a block diagram illustrating an architecture of an analysismodule according to some embodiments of the present disclosure;

FIG. 6 is a flowchart of a process for determining a personalized modeland calculating blood pressure of a subject according to someembodiments of the present disclosure;

FIG. 7 illustrates an exemplary personal health manager according tosome embodiments of the present disclosure;

FIG. 8 provides exemplary processing regarding calculating bloodpressure of a subject based on effective physiological features of thesubject according to some embodiments of the present disclosure;

FIG. 9 depicts the architecture of a mobile device that may be used toimplement a specialized system or a part thereof incorporating thepresent disclosure;

FIG. 10 depicts the architecture of a computer that may be used toimplement a specialized system or a part thereof incorporating thepresent disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure.

The present disclosure relates to system, method, and programmingaspects of physiological feature derivation, for example, blood pressuremonitoring. The system and method involve improved signal processing andmodel monitoring. The system and method as disclosed herein may monitormultiple physiological features. The characteristics of the system andmethod may include, for example, real time, simultaneity, continuity,non-invasiveness, improved accuracy, or the like, or a combinationthereof. In some embodiments, the system and method as disclosed hereinmay monitor various cardiovascular activities and related informationincluding, for example, blood pressure information, electrocardiography(ECG) information, blood oxygenation information, or the like, or acombination thereof. In some embodiments, a blood pressure may beestimated based on pulse wave related information, for example, pulsetransit time (PTT), pulse arrival time (PAT), Fourier spectrum of thepulse wave, wavelet decomposition of the pulse wave, first orderderivative and higher order derivatives of the pulse wave, or the like,or a combination thereof. In some embodiments, a blood pressure and/orblood oxygen level may be estimated based on photoplethysmogram (PPG)signals. The system and method as disclosed herein may be used in ahealthcare institute (e.g., a hospital) or at home. The followingdescription is provided with reference to the derivation and reductionof physiological features in connection with the blood pressuremonitoring for illustration purposes, and is not intended to limit thescope of the present disclosure. Merely by way of example, the systemand method as disclosed herein may utilize one or more other pulse waverelated processing, for example, artificial intelligence, big data basedneural networks, and the like, for blood pressure monitoring.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawing(s), allof which form a part of this specification. It is to be expresslyunderstood, however, that the drawing(s) are for the purposes ofillustration and description only and are not intended to limit thescope of the present disclosure. As used in the specification and in theclaims, the singular form of “a,” “an,” and “the” include pluralreferents unless the context clearly dictates otherwise.

FIG. 1 illustrates an exemplary system configuration in which a system100 may be deployed in accordance with some embodiments of the presentdisclosure. The system 100 may monitor one or more physiological pulsewave signals of interest. The system 100 may include a measuring device110, a server 120, an external data source 70, and a terminal 140.Various components of the system 100 may be connected to each otherdirectly or indirectly via a network 150.

The measuring device 110 may measure a signal. The signal may be acardiovascular signal. The signal may relate to or be used to calculateor estimate physiological features of interest. In some embodiments, thesignal may be a photoplethysmogram (PPG) signal. The physiologicalfeatures may be one or more spatial, temporal, spectral, and/or personalquantities associated with the PPG signal. For example, thephysiological features may include pulse transit time (PTT). Themeasuring device 110 may include, for example, a clinical device, ahousehold device, a portable device, a wearable device, or the like, ora combination thereof. As used herein, a clinical device may be one thatmeets applicable standards and/or specifications to be used in aclinical setting including, for example, a hospital, a doctor's office,a nursing home, or the like. A clinical device may be used by or withthe assistance of a healthcare provider. As used herein, a householddevice may be one that meets applicable standards and/or specificationsto be used at home or a nonclinical setting. A household device may beused by someone who is or is not a professional provider. A clinicaldevice or a household device, or a portion thereof, may be portable orwearable. Exemplary clinical devices include an auscultatory device, anoscillometric device, an ECG monitor, a PPG monitor, or the like, or acombination thereof. Exemplary household devices include anoscillometric device, a household ECG monitor, a sphygmometer, or thelike, or a combination thereof. Exemplary portal devices include anoscillometric device, a portable ECG monitor, a portable PPG monitor, orthe like, or a combination thereof. Exemplary wearable devices include apair of glasses 111, a shoulder strap 112, a smart watch 17, an anklet114, a thigh band 115, an armband 116, a chest belt 117, a necklet 118,a finger clip (not shown), or the like, or a combination thereof. Theabove mentioned examples of measuring devices 110 are provided forillustration purposes, and not intended to limit the scope of thepresent disclosure. A measuring device 110 may be in another formincluding, for example, a fingerstall, a wristband, a brassiere, anunderwear, a chest band, or the like, or a combination thereof.

Merely by way of example, the measuring device 110 is a wearable orportable device that may measure one or more cardiovascular signals. Insome embodiments, the wearable or portable device may process at leastsome of the measured signals, estimate a physiological feature ofinterest based on the measured signals, display a result including thephysiological feature of interest in the form of, for example, an image,an audio alert, perform wired or wireless communication with anotherdevice or server (for example, the server 120), or the like, or acombination thereof. In some embodiments, the wearable or portabledevice may communicate with another device (for example, the terminal140) or a server (for example, a cloud server). The device or server mayprocess at least some of the measured signals, estimate a physiologicalfeature of interest based on the measured signals, display a resultincluding the physiological feature of interest in the form of, forexample, an image, an audio alert, or the like, or a combinationthereof.

In some embodiments, the operations of processing the measured signals,estimating a physiological feature, displaying a result, or performingwired or wireless communication may be performed by an integrated deviceor by separate devices connected to or communicating with each other.Such an integrated device may be portable or wearable. In someembodiments, at least some of the separate devices may be portable orwearable, or located in the vicinity of a subject whose signal ismeasured or a physiological feature of interest is estimated ormonitored. As used herein, a subject may refer to a person or animalwhose signal or information is acquired and whose physiological featureis acquired, estimated, or monitored. Merely by way of example, asubject may be a patient whose cardiovascular signals are acquired, andblood pressure estimated or monitored based on the acquiredcardiovascular signals. Merely by way of example, the subject wears themeasuring device 110 that may measure one or more cardiovascularsignals; the measured one or more cardiovascular signals are transmittedto a smart phone that may calculate or estimate one or morephysiological features of interest based on the measured signals. Thecalculated one or more physiological features related to the subject maybe input a personalized model for the subject, and the blood pressure ofthe subject may be calculated based on the one or more physiologicalfeatures and the personalized model for the subject. In someembodiments, at least some of the separate devices are located in alocation remote from the subject. Merely by way of example, the subjectwears the measuring device 110 that may measure one or more signals; themeasured one or more signals are transmitted to a processor that maycalculate or estimate multiple physiological features of interest basedon the measured signals; the calculated or estimated physiologicalfeatures of interest may be provided to the subject, or a user otherthan the subject (for example, a doctor, a care provider, a familymember relating to the subject, or the like, or a combination thereof).

In some embodiments, the measuring devices 110 may include various typesof sensors including, for example, an electrode sensor, an opticalsensor, a photoelectric sensor, a pressure sensor, an accelerometer, agravity sensor, a temperature sensor, a moisture sensor, or the like, ora combination thereof. The measuring device may monitor and/or detectone or more types of variables related to the subject including, forexample, weight, temperature, humidity, user or subject input, or thelike, or a combination thereof. The measuring devices 110 may alsoinclude a positioning system, for example, a GPS receiver, or a locationsensor, and the position information may be transmitted to the server120, the external data source 70, the terminal 140, or the like, or acombination thereof, through the network 150. The position informationand measured signals may be transmitted simultaneously or successively.

The system may include or communicate with a server configured forstoring a library 900 and/or models 121. The server may be the server120. The server 120 may be a cloud server. Merely by way of example, theserver 120 may be implemented in a cloud server that may provide storagecapacity, computation capacity, or the like, or a combination thereof.The library 900 may collect or store personal data. The personal datamay include static data, dynamic data, or both. Exemplary static datamay include various information regarding a subject including identity,contact information, birthday, a health history (for example, whether asubject has a history of smoking, information regarding a prior surgery,a food allergy, a drug allergy, a medical treatment history, a historyof genetic disease, a family health history, or the like, or acombination thereof), the gender, the nationality, the height, theweight, the occupation, a habit (for example, a health-related habitsuch as an exercise habit), the education background, a hobby, themarital status, religious belief, or the like, or a combination thereof.Exemplary dynamic data may include a current health condition of asubject, medications the subject is taking, a medical treatment thesubject is undertaking, diet, or the like, or a combination thereof. Thelibrary 900 may also include personal calibration data regarding asubject. For example, caliphysiological signals or features (forexample, pulse transit time (PTT), systolic blood pressure (SBP),diastolic blood pressure (DBP), or the like) relating to the subject formultiple time points or over a period of time, or the like, or acombination thereof.

The library 900 may be stored locally on a measuring device 110, or aterminal 140. The library 900 may include different sections (e.g., apersonal data, a universal data, or the like) with different accesscontrol level. For example, personal data may record data andinformation associated with each individual users, but a subject mayhave different access permits to different parts of personal data. Forexample, Subject 1's personal data, Subject 2's personal data, andSubject N's personal data may be stored in the library 900, but Subject1 may only have full access to his/her personal data and limited accessto other user's personal data.

The personal data may further include, but not limited to, headers,histories, and preferences. Additionally, a header may have a subject'sbasic information and medical records. A header may include, but notlimited to, subject's age, gender, race, occupation, health condition,medical history, life style, marital status, and other personalinformation. A history may record measured data (M), calibration values(C) (or calibration data), results (SBP, DBP, BP) and additionalinformation associated with previous measurement and/or calibration.Furthermore, additional information may be any internal or externalvariables occurred when a subject is conducting a measurement and/orcalibration. External variables may include, room temperature, humidity,air pressure, weather, climate, time, and date, etc. Internal variablessuch as, body temperature, metabolism rate, mood, level of activity,type of activity, diet, and health condition, etc. The above mentionedexamples of additional information are only to provide a betterillustration, additional information associated with each measurementand/or calibration may be other types of information, such as viscosityand other rheological data of a subject's blood. In some embodiments,the concepts of additional information and information recorded in aheader are interchangeable. When some information originally recorded ina header changes with each measurements, it may also be considered asadditional information.

Preference may have information associated with models, for example, asubject's favorite models and coefficients, and favorite modelsapplicability, indicating which favorite model(s) are used under whatkind of conditions or with what additional information. A subject'shistorical data may refer to all the information stored under a history.Preference may also include a rating of a subject, which rates thereliability of the subject's personal data and may be considered as aweight factor when sorting the subject's personal data into peer data.For example, a subject who uploads calibration values (C) every week mayhave a better rating as compared to another subject who only calibratesonce every year. The above mentioned examples of information recorded ina preference, and a preference may include other information, such aswhich part of personal data a subject is willing to share with otherusers or organizations.

The universal data may include some non-private or non-personalizeddata, which may be accessed by other users or subjects. The universaldata may include the records of the database of all the models, logics,and public data, for example, models and coefficients, logical judgmentsto sort peer data from personal data, and statistical results related tocalibration values. Peer data may be sorted from multiple subjects'personal data, and logical judgments to sort peer data from personaldata serve to find most closely related data according the subjects'headers, and additional information in histories. Logical judgments tosort peer data from personal data may also consider ratings inpreferences to weigh the data acquired from different subjects. Theabove mentioned examples of information recorded in the universal dataare only to provide a better illustration, and the universal data mayalso include other information such as errors (E, E′, E″) associatedwith each regression analysis. More description may be found in, forexample, International Application No. PCT/CN2015/083334 filed Jul. 3,2015 and International Application No. PCT/CN2015/096498 filed Dec. 5,2015, which are hereby incorporated by reference.

One or more models 121 in the server 120 may be applied in dataprocessing or analysis, as described elsewhere in the presentdisclosure. The description of the server 120 above is provided forillustration purposes, and not intended to limit the scope of thepresent disclosure. The server 120 may have a different structure orconfiguration. For example, models 121 are not stored in the server 120;instead, the models 121 may be stored locally at the terminal 140.Furthermore, a library 900 may also be stored at the terminal 140.

The external data sources 70 may include a variety of organizations,systems, and devices, or the like, or a combination thereof. Exemplarydata sources 70 may include a medical institution 71, a researchfacility 72, a database 73, and a peripheral device 74, or the like, ora combination thereof. The medical institution 71 or the researchfacility 72 may provide, for example, personal medical records, clinicaltest results, experimental research results, theoretical or mathematicalresearch results, models suitable for processing data, or the like, or acombination thereof. The database 73 may store various data related to asubject, such as physiological features and personal data related to thesubject. A peripheral device 74 may monitor and/or detect one or moretypes of variables including, for example, temperature, humidity, useror subject input, or the like, or a combination thereof. The abovementioned examples of the external data sources 70 and data types areprovided for illustration purposes, and not intended to limit the scopeof the present disclosure. For instance, the external data sources 70may include other sources and other types of data, such as geneticinformation relating to a subject or his family.

The terminal 140 in the system 100 may be configured for processing atleast some of the measured signals, estimating a physiological featureof interest based on the measured cardiovascular signals, displaying aresult including the physiological feature of interest in the form of,for example, an image, storing data, controlling access to the system100 or a portion thereof (for example, access to the personal datastored in the system 100 or accessible from the system 100), managinginput-output from or relating to a subject, or the like, or acombination thereof. The terminal 140 may include, for example, a mobiledevice 141 (for example, a smart phone, a tablet, a laptop computer, orthe like), a personal computer 142, other devices 143, or the like, or acombination thereof. Other devices 143 may include a device that maywork independently, or a processing unit or processing module assembledin another device (for example, an intelligent home terminal). Merely byway of example, the terminal 140 includes a CPU or a processor in ameasuring device 110. In some embodiments, the terminal 140 may includean engine 200 as described in FIG. 2, and the terminal 140 may alsoinclude a measuring device 110.

The network 150 may be a single network or a combination of differentnetworks. For example, the network 150 may be a local area network(LAN), a wide area network (WAN), a public network, a private network, aproprietary network, a Public Telephone Switched Network (PSTN), theInternet, a wireless network, a virtual network, or any combinationthereof. The network 150 may also include various network access points,for example, wired or wireless access points such as base stations orInternet exchange points (not shown in FIG. 1), through which a datasource or any component of the system 100 described above may connect tothe network 150 in order to transmit information via the network 150.

Various components of or accessible from the system 100 may include amemory or electronic storage media. Such components may include, forexample, the measuring device 110, the server 120, the external datasources 70, the terminal 140, peripheral device 74 discussed inconnection with FIG. 2, or the like, or a combination thereof. Thememory or electronic storage media of any component of the system 100may include one or both of a system storage (for example, a disk) thatis provided integrally (i.e. substantially non-removable) with thecomponent, and a removable storage that may be removably connected tothe component via, for example, a port (for example, a USB port, afirewire port, etc.) or a drive (for example, a disk drive, etc.). Thememory or electronic storage media of any component of the system 100may include or be connectively operational with one or more virtualstorage resources (for example, cloud storage, a virtual privatenetwork, and/or other virtual storage resources).

The memory or electronic storage media of the system 100 may include adynamic storage device that may store information and instructions to beexecuted by the processor of a system-on-chip (SoC, for example, achipset including a processor), other processors (or computing units),or the like, or a combination thereof. The memory or electronic storagemedia may also be used to store temporary variables or otherintermediate information during execution of instructions by theprocessor(s). Part of or the entire memory or electronic storage mediamay be implemented as Dual In-line Memory Modules (DIMMs), and may beone or more of the following types of memory: static random accessmemory (SRAM), Burst SRAM or Synch Burst SRAM (BSRAM), dynamic randomaccess memory (DRAM), Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM(EDRAM), Extended Data Output RAM (EDO RAM), Extended Data Output DRAM(EDO DRAM), Burst Extended Data Output DRAM (BEDO DRAM), Enhanced DRAM(EDRAM), synchronous DRAM (SDRAM), JEDECSRAM, PCIOO SDRAM, Double DataRate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Sync Link DRAM(SLDRAM), Direct Rambus DRAM (DRDRAM), Ferroelectric RAM (FRAM), or anyother type of memory device. The memory or electronic storage media mayalso include read-only memory (ROM) and/or another static storage devicethat may store static information and instructions for the processor ofthe SoC and/or other processors (or computing units). Further, thememory or electronic storage media may include a magnetic disk, opticaldisc or flash memory devices to store information and instructions.

In some embodiments, the SoC may be part of a core processing orcomputing unit of a component of or accessible from the system 100. TheSoC may receive and process input data and instructions, provide outputand/or control other components of the system. In some embodiments, theSoC may include a microprocessor, a memory controller, a memory, and aperipheral component. The microprocessor may further include a cachememory (for example, SRAM), which along with the memory of the SoC maybe part of a memory hierarchy to store instructions and data. Themicroprocessor may also include one or more logic modules such as afield programmable gate array (FPGA) or other logic array. Communicationbetween the microprocessor in the SoC and memory may be facilitated bythe memory controller (or chipset), which may also facilitate incommunicating with the peripheral component, such as a counter-timer, areal-time timer, a power-on reset generator, or the like, or acombination thereof. The SoC may also include other componentsincluding, for example, a timing source (for example, an oscillator, aphase-locked loop, or the like), a voltage regulator, a power managementcircuit, or the like, or a combination thereof.

Merely by way of example, the system 100 may include a wearable orportable device. The wearable or portable device may include a SoC and aplurality of sensors. Exemplary sensors may include a photoelectricsensor, a conductance sensor, or the like, or a combination thereof. TheSoC may process signals acquired through at least some of the pluralityof sensors. The acquired signals may be various physiological signalsincluding, for example, photoplethysmograph (PPG), electrocardiograph(ECG), or the like, or a combination thereof. The SoC may calculate aphysiological feature of interest based on the acquired signals.Exemplary physiological features of interest may be blood pressure,blood oxygen level, ECG information, heart rate, or the like, or acombination thereof.

In some embodiments, the external data source 70 may receive data fromthe measuring device 110, the sever 120, the terminal 140, or the like,or any combination by the network 150. Merely by way of example, theexternal data source 70 (for example, a medical institution, or a smarthome system, or the like) may receive information relating to a subject(for example, location information, data from the cloud sever or aterminal, or the like, or a combination thereof) based on the datareceived from the measuring devices 110 or the terminals 140. In someembodiments, the measuring device 110 may receive data from the sever120, the external data source 70, or the like, or any combination, viathe network 150. Merely by way of example, the measuring device 110 mayreceive the information relating to a subject (for example, acurrent/historical health condition of a subject, medications thesubject is taking, medical treatment the subject is undertaking,current/historical diets, current emotion status, historicalphysiological features (for example, PTT, SBP, DBP) relating to thesubject, or the like, or a combination thereof). Furthermore, theterminal 140 may receive data from the measuring device 110, the server120, the external data source 70, or the like, or a combination thereof.

FIG. 1 is a specific example of the system 100, and the configuration ofthe system 100 is not limited to that illustrated in FIG. 1. Forexample, a server 120 may be omitted, migrating all of its functions toa terminal 140. In another example, a server 120 and a terminal 140 mayboth be omitted, migrating all of their functions to a measuring device110. The system may include various devices or combinations of devicesin different embodiments.

In an example, the system may include a wearable or portable device anda mobile device (for example, a smart phone, a tablet, a laptopcomputer, or the like). The wearable or portable device may be used toacquire physiological signals, environmental information, or the like,or a combination thereof. The mobile device may be used to receive thesignals or information acquired by the wearable or portable device. Themobile device may calculate one or more physiological features ofinterest based on the acquired signals or information, as well asrelevant data retrieved from another source (for example, from a server,a memory incorporated in the wearable or portable device, a memoryincorporated in the mobile device, etc.). The retrieved relevant datamay include, for example, current/historical information stored on theserver. Exemplary current/historical information may include acurrent/historical health condition of a subject, current/historicalmedications the subject is/was taking, current/historical medicaltreatment the subject is/was undertaking, current/historical diets,current/historical emotion status, current/historical physiologicalfeatures (for example, PTT, SBP, DBP, ECG information, heart rate, bloodoxygen level) relating to the subject, or the like, or a combinationthereof. The wearable or portable device, or the mobile device maydisplay or report, or store at least some of the acquired signals,information, the retrieved relevant data, the calculated one or morephysiological features of interest, or the like, or a combinationthereof. The display or report may be provided to a subject, a userother than the subject, a third party, the server, or another device.

In another example, the system may include a wearable or portable devicethat may perform functions including: acquiring physiological signals orenvironmental information, retrieving relevant data from another source(for example, from a server, a memory incorporated in the wearable orportable device, etc.), calculating one or more physiological featuresrelated to a subject based on the acquired signals, information, or theretrieved relevant data, determining a personalized model for thesubject, computing the blood pressure of the subject based on thepersonalized model and the one or more physiological features related toa subject, displaying, reporting, or storing at least some of theacquired signals, information, the retrieved relevant data, thecalculated one or more physiological features of interest, the bloodpressure of the subject, or the like, or a combination thereof. Thedisplay or report may be provided to the subject, a user other than thesubject, a third party, the server, or another device.

In a further example, the system may include a wearable or portabledevice that may perform functions including: acquiring physiologicalsignals related to a subject and environmental information,communicating with a server to transmit at least some of the acquiredsignals or information to the server such that the server may calculateone or more physiological features of the subject, determining apersonalized model for the subject, computing the blood pressure of thesubject based on the personalized model and the one or morephysiological features related to a subject, receiving the calculatedone or more physiological features and/or the blood pressure of thesubject from the server, displaying, reporting or storing at least someof the acquired signals, information, the calculated one or morephysiological features of interest, the blood pressure of the subject,or the like, or a combination thereof. The display or report may beprovided to the subject, a user other than the subject, a third party,the server, or another device. In some embodiments, the communicationbetween the wearable or portable device and the server may be achievedby way of the wearable or portable device being connected to a network(for example, the network 150). In some embodiments, the communicationbetween the wearable or portable device and the server may be achievedvia a communication device (for example, a mobile device such as a smartphone, a tablet, a laptop computer, or the like) that communicates withboth the wearable or portable device and the server.

In still a further example, the system may include a wearable orportable device, a mobile device (for example, a smart phone, a tablet,a laptop computer, or the like), and a server. The wearable or portabledevice may be used to acquire physiological signals, environmentalinformation, or the like, or a combination thereof. The mobile devicemay be used to receive the signals or information acquired by thewearable or portable device, and may calculate one or more physiologicalfeatures of interest based on the received signals and/or informationretrieved from the wearable or portable device, as well as relevant dataretrieved from, for example, a server, a memory incorporated in thewearable or portable device or incorporated in the mobile device. Themobile device may display, report, or store at least some of theacquired signals, information, the retrieved relevant data, thecalculated one or more physiological features of interest, or the like,or a combination thereof. The display or report may be provided to asubject, a user other than the subject, a third party, the server, oranother device.

In still a further example, the system may include an integratedclinical device or a household device. The integrated device may bewearable or portable. The integrated device may be used to acquirephysiological signals, environmental information, or the like, or acombination thereof. The integrated device may further include an outputdevice that may display, report, or output at least some of the acquiredsignals, information, the retrieved relevant data, the calculated one ormore physiological features of interest, or the like, or a combinationthereof. The display or report may be provided to a subject, a userother than the subject, a third party, the server, or another device.The integrated device may perform one or more measurements forcalibrating the integrated device.

In still a further example, the system may include an integratedclinical device or a household device and a server. The integrateddevice may be wearable or portable. The integrated device may performfunctions including: acquiring physiological signals and environmentalinformation, communicating with a server to transmit at least some ofthe acquired signals or information to the server such that the servermay calculate one or more physiological features of interest, receivingthe calculated one or more physiological features of interest from theserver, displaying, reporting or storing at least some of the acquiredsignals, information, the calculated one or more physiological featuresof interest, or the like, or a combination thereof. The display orreport may be provided to a subject, a user other than the subject, athird party, the server, or another device. The integrated device mayperform one or more measurements for calibrating the integrated device.In some embodiments, the communication between the integrated clinicaldevice or the household device and the server may be achieved by way ofthe integrated clinical device or the household device being connectedto a network (for example, the network 150). In some embodiments, thecommunication between the integrated device and the server may beachieved via a communication device (for example, a mobile device suchas a smart phone, a tablet, a laptop computer, or the like) thatcommunicates with both the wearable or portable device and the server.

In some embodiments, the system may provide a user interface to allow asubject, a user other than the subject, or an entity to exchangeinformation (including input into or output from the system) with thesystem as disclosed herein. The user interface may be implemented on aterminal device including, for example, a mobile device, a computer, orthe like, or a combination thereof. The user interface may be integratedin the system, e.g., a display device of the system. The access to thesystem may be allowed to one who has an appropriate access privilege. Anaccess privilege may include, for example, a privilege to read some orall information relating to a subject, update some or all informationrelating to a subject, or the like, or a combination thereof. The accessprivilege may be associated with or linked to a set of logincredentials. Merely by way of example, the system may provide threetiers of access privileges. A first tier may include a full accessprivilege regarding information relating to a subject, allowing bothreceiving and updating information relating to a subject. A second tiermay include a partial access privilege regarding information relating toa subject, allowing receiving and updating part of information relatingto a subject. A third tier may include a minimal access privilegeregarding information relating to a subject, allowing receiving orupdating part of information relating to a subject Different logincredentials may be associated with different access privilege to theinformation relating to a subject in the system. As used herein,updating may include providing information that does not exist in thesystem, or modifying pre-existing information with new information.

Merely by way of example, the system may receive information relating toa subject provided via the user interface. The information relating to asubject may include basic information and optional information.Exemplary basic information may include the height, the weight, the age(or the date of birth), the gender, the arm length, the nationality, theoccupation, a habit (for example, a health-related habit such as anexercise habit), the education background, a hobby, the marital status,religious belief, a health-related history (for example, whether asubject has a history of smoking, a food allergy, a drug allergy, amedical treatment history, a family health history, a history of geneticdisease, information regarding a prior surgery, or the like, or acombination thereof), contact information, emergency contact, or thelike, or a combination thereof. Exemplary optional information mayinclude, current health condition of the subject, medications thesubject is taking, a medical treatment the subject is undertaking, diet.The system may receive, via the user interface, information relating toa specific measurement of, for example, a physiological feature ofinterest. Examples of such information may include the motion state ofthe subject at or around the acquisition time (defined elsewhere in thepresent disclosure), the emotional state at or around the acquisitiontime, the stress level at or around the acquisition time, or the like,or a combination thereof. The system may receive, via the userinterface, one or more options or instructions. In some embodiments, theoptions or instructions may be provided by a subject or a user otherthan the subject answering questions or making selections in response toquestions or prompts by the system. In one example, the options orinstructions may include a measurement frequency (for example, once aweek, once a month, twice a week, twice a month, once a day, twice aday, or the like), a preferred format of the presentation of informationto the subject or a user other than the subject (for example, email, avoice message, a text message, an audio alert, haptic feedback, or thelike, or a combination thereof). In another example, the options orinstructions may include information relating to calculating features ofinterest, for example, rules regarding how to select a model, afunction, calibration data, or the like, or a combination thereof.

In some embodiments, the system may provide, via the user interface,information to a subject, or a user other than the subject. Exemplaryinformation may include an alert, a recommendation, a reminder, or thelike, or a combination thereof. In one example, an alert may be providedor displayed to the subject or a user other than the subject if atriggering event occurs. Exemplary triggering events may be that atleast some of the acquired information or a physiological feature ofinterest exceeds a threshold. Merely by way of example, a triggeringevent may be that the acquired heart rate exceeds a threshold (forexample, higher than 150 beats per minute, lower than 40 beats perminute, or the like). As another example, a triggering event may be thatthe physiological feature of interest, for example, an estimated bloodpressure, exceeds a threshold. In another example, a recommendation maybe provided or displayed to the subject or a user other than thesubject. Exemplary recommendations may be a request to input specificdata (for example, basic information, optional information, updatedfeatures of interest, updated models, updated functions, updated optionsand instructions, or the like, or a combination thereof). A reminder maybe provided or displayed to the subject or a user other than thesubject. Exemplary reminders may include a reminder to take aprescription medication, take a rest, take a measurement of aphysiological feature of interest, or the like, or a combinationthereof.

In some embodiments, the system may communicate with the subject, a userother than the subject, and/or a third party through the user interface.Exemplary third parties may be a doctor, a healthcare worker, a medicalinstitution, a research facility, a peripheral device of the subject ora user well-connected to the subject, or the like. Exemplarycommunications may relate to the health conditions of the subject, adietary habit, an exercise habit, a prescription medication,instructions or steps to conduct a measurement, or the like, or acombination thereof. In some embodiments, a user interface accessible toor by a third party may be the same as, or different from a userinterface accessible to or by a subject. In one example, an output ordata may be transmitted to a third party (for example, a computer, aterminal at a doctor's office, a hospital where a health care provideris located and the health condition of the subject is being monitored,or the like, or a combination thereof). The third party may providefeedback information or instructions related to the output informationvia the user interface. Merely by way of example, a third party mayreceive information regarding one or more physiological features ofinterest relating to a subject, and accordingly provide a recommendationof actions to be taken by the subject (for example, to take aprescription medication, to take a rest, to contact or visit the thirdparty, or the like, or a combination thereof); the system may relay therecommendation to the subject.

FIG. 2 shows an exemplary diagram including the engine 200. The engine200 may be configured for acquiring one or more signals related to asubject and calculating or estimating blood pressure of the subjectbased on one or more physiological features derived from the acquiredsignals. As illustrated, the engine 200 may be connected to or otherwisecommunicate with, for example, measuring device 110, the database 73,and the server 120. The engine 200 may include an informationacquisition module 210, an analysis module 220, and an output module230. The information acquisition module 210 may be configured foracquiring a signal or information relating to a subject, for example, aphysiological signal, information relating to the health condition ofthe subject, or the like, or a combination thereof. The analysis module220 may be configured for analyzing the acquired signal or information,or determining or estimating physiological features of interest, ordetermining a personalized model for a subject, or determining the bloodpressure of the subject based on the personalized model. The outputmodule 230 may be configured for outputting the acquired signal orinformation, the physiological feature of interest, the blood pressureof the subject, or the like, or a combination thereof. As used herein, amodule may have an independent processor, or use system sharedprocessor(s). The processor(s) may perform functions according toinstructions related to various modules. For example, the analysismodule 220, according to relevant instructions, may retrieve acquiredsignals and perform calculations to obtain one or more physiologicalfeature of interest.

The information acquisition module 210 may be configured for acquiring asignal or information from or relating to one or more subjects. As usedherein, acquiring may be achieved by way of receiving a signal orinformation sensed, detected, or measured by, for example, a sensor, orby way of receiving an input from a subject or from a user other thanthe subject (for example, a doctor, a care provider, a family memberrelating to the subject, or the like, or a combination thereof). Forbrevity, an acquired signal or information may be referred to asacquired information. As used herein, information may include a signalrelating to a subject that is acquired by a device including, forexample, a sensor, environmental information that is acquired by adevice including, for example, a sensor, information that is acquiredotherwise including, for example, from an input by a subject or a userother than the subject, a processed or pre-treated information that isacquired as described, or the like, or a combination thereof. Exemplarysensors may include an electrode sensor, an optical sensor, aphotoelectric sensor, a pressure sensor, an accelerometer, a gravitysensor, a temperature sensor, a moisture sensor, or the like, or acombination thereof.

Exemplary acquired information may include physiological information. Inthe exemplary context of determining blood pressure, the physiologicalinformation may include a cardiovascular signal. Exemplarycardiovascular signals may include a photoplethysmogram (PPG) signal, anelectrocardiogram (ECG) signal, a ballistocardiogram (BCG) signal, ablood pressure (BP), a systolic blood pressure (SBP), a diastolic bloodpressure (DBP), a pulse rate (PR), a heart rate (HR), a heart ratevariation (HRV), cardiac murmur, blood oxygen saturation, a density ofblood, a pH value of the blood, a bowel sound, a brainwave, a fatcontent, a blood flow rate, or the like, or a combination thereof.Exemplary acquired information may include information regarding asubject, for example, the height, the weight, the age, the gender, thebody temperature, the arm length, an illness history, or the like, or acombination thereof. Exemplary acquired information may includeinformation from or relating to the ambient surrounding a subject(referred to as environmental information) at or around the acquisitiontime. Exemplary environmental information may include temperature,humidity, air pressure, an air flow rate, an ambient light intensity, orthe like, or a combination thereof. As used herein, the acquisition timemay refer to a time point or a time period when information relating tothe subject, for example, physiological information of the subject, isacquired.

The information acquisition module 210 may receive or load informationfrom the measuring device 110, the server 120, the database 73, or otherdevices (not shown) including, for example, an ECG monitor, a PPGmonitor, a respiratory monitor, a brainwave monitor, a blood oxygenmonitor, a blood glucose monitor, and a device having similar functions.In the disclosure, the term “monitor” and the term “sensor” may be usedinterchangeably. Examples of measuring device 110 may include a smartwatch, a finger clip, an earphone, a pair of glasses, a bracelet, anecklace, or the like, or a combination thereof. The measuring device110, the server 120, the database 73, or other devices may be local orremote. For example, the server 120 and the engine 200 may be connectedthrough a local area network (LAN), or Internet. The measuring device110 and the engine 200 may be connected through a local area network, orInternet. Other devices and the engine 200 may be connected through alocal area network, or Internet. The information transmission betweenthe information acquisition module 210 and the measuring device 110, theserver 120, the database 73, or such other devices may be via a wiredconnection, a wireless connection, or the like, or a combinationthereof.

The information acquisition module 210 may receive information providedby a subject or a user other than the subject via, for example, an inputdevice. The input device may include but is not limited to a keyboard, atouch screen (for example, with haptics or tactile feedback), a speechinput device, an eye tracking input device, a brain monitoring system,or the like, or a combination thereof. The information received throughthe input device may be transmitted to a processor, via, for example, abus, for further processing. The processor for further processing theinformation obtained from the input device may be a digital signalprocessor (DSP), a SoC (system on the chip), or a microprocessor, or thelike, or the combination thereof. Other types of input device mayinclude cursor control device, such as a mouse, trackball, or cursordirection keys to convey information about direction and/or commandselections, for example, to the processor.

The description of the information acquisition module 210 is intended tobe illustrative, and not to limit the scope of the present disclosure.Many alternatives, modifications, and variations will be apparent tothose skilled in the art. The features, structures, methods, and othercharacteristics of the exemplary embodiments described herein may becombined in various ways to obtain additional and/or alternativeexemplary embodiments. For example, a storage unit (not shown in FIG. 2)may be added to the information acquisition module 210 for storing theacquired information.

The analysis module 220 may be configured for analyzing acquiredinformation. The analysis module 220 may be connected to or otherwisecommunicate with one or more information acquisition modules 210-1,210-2, . . . , 210-N to receive at least part of the acquiredinformation. The analysis module 220 may be configured for performingone or more operations including, for example, a pre-processing, acalculation, a calibration, a statistical analysis, or the like, or acombination thereof. Any one of the operations may be performed based onat least some of the acquired information, or an intermediate resultfrom another operation (for example, training data, or an operationperformed by the analysis module 220, or another component of the system100). For instance, the analysis may include one or more operationsincluding pre-processing at least part of the acquired informationrelating to a subject, identifying characteristic points or features ofthe acquired information or the pre-treated information, determining apersonalized model for the subject, calculating the blood pressure ofthe subject, analyzing the information regarding the subject provided bythe subject or a user other than the subject, analyzing the informationregarding the ambient environment surrounding the subject at or aroundthe acquisition time, or the like, or a combination thereof.

Some operations of the analysis may be performed in parallel or inseries. As used herein, a parallel performance may indicate that someoperations of the analysis may be performed at or around the same time;a serial performance may indicate that some operations of the analysismay commence or be performed after other operations of the analysis havecommenced or finished. In some embodiments, a serial performance of twooperations may indicate that one operation commences after the otheroperation has finished. In some embodiments, a serial performance of twooperations may indicate that one operation commences after the otheroperation has commenced, and the two operations partially overlap. Insome embodiments, at least two operations of an analysis may beperformed in parallel. In some embodiments, at least two operations ofan analysis may be performed in series. In some embodiments, some of theoperations of an analysis may be performed in parallel, and some of theoperations may be performed in series.

The analysis, or some operations of the analysis, may be performed inreal time, i.e. at or around the acquisition time. The analysis, or someoperations of the analysis, may be performed after a delay since theinformation is acquired. In some embodiments, the acquired informationis stored for analysis after a delay. In some embodiments, the acquiredinformation is pre-treated and stored for further analysis after adelay. The delay may be in the order of seconds, or minutes, or hours,or days, or longer. After the delay, the analysis may be triggered by aninstruction from a subject or a user other than the subject (forexample, a doctor, a care provider, a family member relating to thesubject, or the like, or a combination thereof), an instruction storedin the system 100, or the like, or a combination thereof. Merely by wayof example, the instruction stored in the system 100 may specify theduration of the delay, the time the analysis is to be performed, thefrequency the analysis is to be performed, a triggering event thattriggers the performance of the analysis, or the like, or a combinationthereof. The instruction stored in the system 100 may be provided by asubject or a user other than the subject. An exemplary triggering eventmay be that at least some of the acquired information or a physiologicalfeature of interest exceeds a threshold. Merely by way of example, atriggering event may be that the acquired heart rate exceeds a threshold(for example, higher than 150 beats per minute, lower than 40 beats perminute, or the like). As used herein, “exceed” may be larger than orlower than a threshold. As another example, a triggering event may bethat the physiological feature of interest, for example, an estimatedblood pressure, exceeds a threshold.

The analysis module 220 may be centralized or distributed. A centralizedanalysis module 220 may include a processor (not shown in FIG. 2). Theprocessor may be configured for performing the operations. A distributedanalysis module 220 may include a plurality of operation units (notshown in FIG. 2). The operation units may be configured for collectivelyperforming the operations of a same analysis. In the distributedconfiguration, the performance of the plurality of operation units maybe controlled or coordinated by, for example, the server 120.

The acquired information, an intermediate result of the analysis, or aresult of the analysis (for example, a physiological feature ofinterest) may be analog or digital. In an exemplary context of bloodpressure monitoring, the acquired information, an intermediate result ofthe analysis, or a result of the analysis (for example, a physiologicalfeature of interest) may include, for example, a PPG signal, an ECGsignal, a BCG signal, a BP, a SBP, a DBP, a PR, a HR, a HRV (heart ratevariation), cardiac murmur, blood oxygen saturation (or referred to asblood oxygen level), a blood density, a pH value of the blood, a bowelsound, a brainwave, a fat content, a blood flow rate, or the like, or acombination thereof.

A result of the analysis, for example, a physiological feature ofinterest regarding a subject, may be influenced by various factors orconditions including, for example, an environmental factor, a factor dueto a physiological condition of a subject, a factor due to apsychological condition of a subject, or the like, or a combinationthereof. One or more of such factors may influence the accuracy of theacquired information, the accuracy of an intermediate result of theanalysis, the accuracy of a result of the analysis, or the like, or acombination thereof. For instance, a physiological feature of interestmay be estimated based on a correlation with the acquired information; afactor due to a physiological condition may cause a deviation from thecorrelation; the factor may influence the accuracy of the physiologicalfeature of interest that is estimated based on the correlation. Merelyby way of example, a cardiovascular signal relating to a subject mayvary with, for example, time, the psychological condition of thesubject, or the like, or a combination thereof. The correlation betweena cardiovascular signal with a physiological feature (for example, thecorrelation between a PPT value and a blood pressure) of a subject mayvary with, for example, the physiological condition of the subject, thepsychological condition of the subject, the ambient surrounding thesubject, or the like, or a combination thereof. Such an influence may becounterbalanced or compensated in the analysis.

In an analysis, information relating to an influencing condition (forexample, environmental information, a physiological condition, apsychological condition, or the like) may be acquired, and a correctionor adjustment may be made accordingly in the analysis process. Merely byway of example, the correction or adjustment may be by way of acorrection factor. For instance, an environmental correction factor maybe introduced into the analysis based on acquired environmentalinformation from or relating to the ambient surrounding a subject at oraround the acquisition time. Exemplary environmental information mayinclude one or more of temperature, humidity, air pressure, an air flowrate, an ambient light intensity, or the like. Exemplary environmentalcorrection factors may include one or more of a temperature correctionfactor, a humidity correction factor, an air pressure correction factor,an air flow rate correction factor, an ambient light intensitycorrection factor, or the like. As another example, the correction oradjustment may be by way of performing a calibration of the correlation(for example, a calibrated model, a calibrated function, or the like)used to estimate the physiological feature of interest. As a furtherexample, the correction or adjustment may be by way of choosing, basedon information relating to an influencing condition, a correlation froma plurality of correlations used to estimate the physiological featureof interest.

This description of the analysis module 220 is intended to beillustrative, and not to limit the scope of the present disclosure. Manyalternatives, modifications, and variations will be apparent to thoseskilled in the art. The features, structures, methods, and othercharacteristics of the exemplary embodiments described herein may becombined in various ways to obtain additional and/or alternativeexemplary embodiments. For example, a cache unit (not shown in FIG. 2)may be added to the analysis module 220 used for storing an intermediateresult or real time signal or information during the processes abovementioned.

The output module 230 may be configured for providing an output. Theoutput may include a physiological feature of interest, at least some ofthe acquired information (for example, the acquired information that isused in estimating the physiological feature of interest), the bloodpressure of a subject, or the like, or a combination thereof. Thetransmission of the output may be via a wired connection, a wirelessconnection, or the like, or a combination thereof. The output may betransmitted real-time once the output is available for transmission. Theoutput may be transmitted after a delay since the output is availablefor transmission. The delay may be in the order of seconds, or minutes,or hours, or days, or longer. After the delay, the output may betriggered by an instruction from a subject, a user other than thesubject, or a related third party, an instruction stored in the system100, or the like, or a combination thereof. Merely by way of example,the instruction stored in the system 100 may specify the duration of thedelay, the time the output is to be transmitted, the frequency output isto be transmitted, a triggering event, or the like, or a combinationthereof. The instruction stored in the system 100 may be provided by asubject or a user other than the subject. An exemplary triggering eventmay be that the physiological feature of interest or that at least someof the acquired information exceeds a threshold. Merely by way ofexample, a triggering event may be that the acquired heart rate exceedsa threshold (for example, higher than 150 beats per minute, lower than40 beats per minute, or the like). As another example, a triggeringevent may be that the physiological feature of interest, for example, anestimated blood pressure, exceeds a threshold.

The output for transmission may be of, for example, an analog form, adigital form, or the like, or a combination thereof. The output may bein the format of, for example, a graph, a code, a voice message, text,video, an audio alert, a haptic effect, or the like, or a combinationthereof. The output may be displayed on a local terminal, or transmittedto a remote terminal, or both. A terminal may include, for example, apersonal computer (PC), a desktop computer, a laptop computer, a smartphone, a smart watch, or the like, or a combination thereof. Merely byway of example, an output may be displayed on a wearable or portabledevice a subject wears, and also transmitted to a computer or terminalat a doctor's office or a hospital where a health care provider islocated and monitors the health condition of the subject.

The output module 230 may include or communicate with a display devicethat may display output or other information to a subject or a userother than the subject. The display device may include a liquid crystaldisplay (LCD), a light emitting diode (LED)-based display, or any otherflat panel display, or may use a cathode ray tube (CRT), a touch screen,or the like. A touch screen may include, for example, a resistance touchscreen, a capacity touch screen, a plasma touch screen, a vectorpressure sensing touch screen, an infrared touch screen, or the like, ora combination thereof.

In some embodiments, a storage module (not shown in FIG. 2) or a storageunit (not shown in FIG. 2) may be integrated in the engine 200. In someembodiments, a storage unit (not shown in FIG. 2) may be integrated inany one of the information acquisition module 210, the analysis module220, or the output module 230. The storage module (not shown in FIG. 2)or the storage unit (not shown in FIG. 2) may be used for storing anintermediate result, or a result of an analysis. The storage module (notshown in FIG. 2) or the storage unit (not shown in FIG. 2) may be usedas a data cache. The storage module (not shown in FIG. 2) or the storageunit (not shown in FIG. 2) may include a hard disk, a floppy disk,selection storage, RAM, DRAM, SRAM bubble memory, thin film memory,magnetic plated wire memory, phase change memory, flash memory, clouddisk, or the like, or a combination thereof. The storage module (notshown in FIG. 2) or the storage unit (not shown in FIG. 2) may includememory or electronic storage media described in connection with FIG. 1and elsewhere in the present disclosure.

In some embodiments, the engine 200 does not include a storage module ora storage unit, and the measuring device 110 or the server 120 may beused as a storage device accessible by the engine 200. The server 120may be a cloud server providing cloud storage. As used herein, cloudstorage is a model of data storage where digital data are stored inlogical pools, physical storage spanning multiple servers (and oftenlocated at multiple locations). The physical environment including, forexample, the logical pools, the physical storage spanning multipleservers may be owned and managed by a hosting company. The hostingcompany may be responsible for keeping the data available andaccessible, and the physical environment protected and running. Suchcloud storage may be accessed through a cloud service, a web serviceapplication programming interface (API), or by applications that utilizethe API. Exemplary applications include cloud desktop storage, a cloudstorage gateway, a Web-based content management system, or the like, ora combination thereof. The server 120 may include a public cloud, apersonal cloud, or both. For example, the acquired information may bestored in a personal cloud that may be accessed after authorization byway of authenticating, for example, a username, a password, a secretcode, or the like, or a combination thereof. Non personalizedinformation including, for example, methods or calculation models, maybe stored in a public cloud. No authorization or authentication isneeded to access the public cloud. The information acquisition module210, the analysis module 220 and the output module 230 may retrieve orload information or data from the public cloud or the personal clouds.Any one of these modules may transmit signals and data to the publiccloud or personal cloud.

Connection or transmission between any two of the informationacquisition module 210, the analysis module 220, and the output module230 may be via a wired connection, a wireless connection, or the like,or a combination thereof. At least two of these modules may be connectedwith different peripheral equipment. At least two of these modules maybe connected with the same peripheral equipment. The measuring device110 may be connected with one or more modules via a wired connection, awireless connection, or the like, or a combination thereof. Thoseskilled in the art should understand that the above embodiments are onlyutilized to describe the invention in the present disclosure. There aremany modifications and variations to the present disclosure withoutdeparting the spirit of the invention disclosed in the presentdisclosure. For example, the information acquisition module 210 and theoutput module 230 may be integrated in an independent module configuredfor acquiring and outputting signals or results. The independent modulemay be connected with the analysis module 220 via a wired connection, awireless connection, or the like, or a combination thereof. The threemodules in the engine 200 may be partially integrated in one or moreindependent modules or share one or more units.

The connection or transmission between the modules in the system 100, orbetween the modules and the measuring device 110, or between the systemand the server 120 should not be limited to the descriptions above. Allthe connections or transmissions may be used in combination or may beused independently. The modules may be integrated in an independentmodule, i.e. functions of the modules may be implemented by theindependent module. Similarly, one or more modules may be integrated ona single piece of measuring device 110. Any one of the connections ortransmissions mentioned above may be via a wired connection, a wirelessconnection, or the like, or a combination thereof. For example, thewired connection or wireless connection may include, for example, awire, a cable, satellite, microwave, blue tooth, radio, infrared, or thelike, or a combination thereof.

The engine 200 may be implemented on one or more processors. The modulesor units of the engine 200 may be integrated in one or more processors.For example, the information acquisition module 210, the analysis module220, and the output module 230 may be implemented on one or moreprocessors. The one or more processors may transmit signals or data witha storage device (not shown in FIG. 2), the peripheral equipment 240,and the server 120. The one or more processors may retrieve or loadsignals, information, or instructions from the storage device (not shownin FIG. 2), or the server 120, and process the signals, information,data, or instructions, or a combination thereof, to calculate one ormore physiological features of interest. The one or more processors mayalso be connected or communicate with other devices relating to thesystem 100, and transmit or share signals, information, instructions,the physiological features of interest, or the like with such otherdevices via, for example, a mobile phone APP, a local or remoteterminal, or the like, or a combination thereof.

FIG. 3 is a flowchart showing an exemplary process for derivingphysiological features of a subject and estimating blood pressure of thesubject according to some embodiments of the present disclosure.Information regarding the subject may be acquired in step 310. Theinformation acquisition may be performed by the information acquisitionmodule 210. The acquired information may include physiologicalinformation of the subject, environmental information relating to theambient surrounding the subject at or around the acquisition time,information provided by the subject or a user other than the subject.The acquired information may include a PPG signal, an ECG signal, apulse rate, a heart rate, a heart rate variation, blood oxygensaturation, respiration, muscle state, skeleton state, a brainwave, ablood lipid level, a blood sugar level, the height, the weight, the age,gender, the body temperature, the arm length, an illness history, theroom temperature, humidity, air pressure, an air flow rate, the ambientlight intensity, or the like, or a combination thereof. At least some ofthe acquired information may be analyzed at 320. Via the analysis,various features of at least some of the acquired information may beidentified. For example, the acquired information may include a PPGsignal and an ECG signal; the identified features of these signals mayinclude, for example, waveform, characteristic points, peak points,valley points, amplitude, time intervals, phase, frequencies, cycles, orthe like, or a combination thereof. Analysis based on the identifiedfeatures may be carried out in step 320. For example, the physiologicalquantity of interest may be calculated or estimated based on theidentified features. The physiological quantity of interest estimatedbased on the acquired PPG signal and ECG signal may include, forexample, the mean, absolute mean, variance, standard deviation, and/ormedian, of the BP, the SBP, the DBP, the blood oxygen level, or thelike, or a combination thereof. The physiological quantity of interestmay be used for selecting a personalized model for the subject. Themodel may be used to calculate the blood pressure of the subject. Thenthe information regarding the blood pressure of the subject, for examplethe BP, the SBP, the DBP, the blood oxygen level, or the like, or acombination thereof, may be outputted in step 330. Some of the acquiredinformation may be outputted in step 330 as well. The output may bedisplayed to the subject or a user other than the subject, printed,stored in a storage device or the server 120, transmitted to a devicefurther process, or the like, or a combination thereof. It should benoted that after analysis in step 320, a new acquisition step may beperformed in step 310.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, apre-processing step may be added between step 310 and step 320. In thepre-processing step, the acquired signals may be pre-processed, in orderto reduce or remove noise or interferences in the signals originallyacquired. For example, a sophisticated, real-time digital filtering maybe used to reduce or remove high-frequency noise from the PPG or ECGsignal, allowing their features to be accurately identified. Exemplarypre-treatment methods may include low-pass filtering, band-passfiltering, wavelet transform, median filtering, morphological filtering,curve fitting, Hilbert-Huang transform, or the like, or a combinationthereof. Descriptions regarding methods and systems for reducing orremoving noise from a physiological signal, for example, a PPG signal oran ECG signal, may be found in, for example, International PatentApplication Nos. PCT/CN2015/077026 filed Apr. 20, 2015,PCT/CN2015/077025 filed Apr. 20, 2015, and PCT/CN2015/079956 filed May27, 2015, each of which is incorporated by reference. One or more otheroptional steps may be added between step 310 and step 320, or elsewherein the exemplary process illustrated in FIG. 3. Examples of such stepsmay include storing or caching the acquired information.

FIG. 4 is a block diagram illustrating an architecture of an informationacquisition module according to some embodiments of the presentdisclosure. The information acquisition module 210 may be connected toor otherwise communicate with, for example, the peripheral equipment240, the analysis module 220, the output module 230, and the server 120through the network 150. The information acquisition module 210 may beconfigured for acquiring information relating to the subject,information provided by the subject, a user other than the subject,and/or a related third party (for example, a doctor, a healthcareworker, a medical institution, a research facility, a peripheral deviceof the subject or a user well-connected to the subject, or the like),environmental information from the ambient surrounding the subject at oraround the acquisition time, or the like, or a combination thereof. Theinformation acquisition module 210 may include a first acquisition unit410 and a second acquisition unit 420. The first acquisition unit 410may be configured for acquiring a first signal or first informationincluding a first signal relating to the subject. The second acquisitionunit 420 may be configured for acquiring a second signal or secondinformation including a second signal relating to the subject. The firstacquisition unit 410 and the second acquisition unit 420 may acquiresignals in real time. The first signal and the second signal may beacquired simultaneously, at or around the same time. In someembodiments, other than the first acquisition unit 410 and the secondacquisition unit 420, the information acquisition module 210 may includeone or more other acquisition units (not shown in FIG. 4). In someembodiments, the first acquisition unit 410 and the second acquisitionunit 420 may be integrated in an independent module or unit.

In some embodiments, the first acquisition unit 410 may be configuredfor acquiring an ECG signal of the subject. The first acquisition unit410 may include an ECG monitor (not shown in FIG. 4). The ECG monitor(not shown in FIG. 4) may be of any type, e.g., a clinical device, ahouse device, a wearable device, a portable device, or the like. The ECGmonitor (not shown in FIG. 4) may include a plurality of electrodes usedfor recording the variations in the electrical potential relating to thecardiovascular activity of the subject. The electrodes may be arrangedin a 12-lead form, a 5-lead form, a 3-lead form, or the like. Theelectrodes may be located on one or more limbs and/or on the chest ofthe subject. For instance, in the 5-lead form, the electrodes may belocated on the chest of the subject. In some embodiments, the firstacquisition unit 410 may include a control unit (not shown in FIG. 4).The control unit (not shown in FIG. 4) may be configured for controllinga feature of the acquisition process. The feature may include samplingfrequency, sampling time interval, or the like, or a combinationthereof. In some embodiments, the first acquisition unit 410 may includea storage unit (not shown in FIG. 4). The storage unit (not shown inFIG. 4) may be used for storing the acquired first signals, thefeatures, or the like, or a combination thereof. In some embodiments,the acquired signals, the features may be stored in any storage devicedisclosed anywhere in the present disclosure.

In some embodiments, the second acquisition unit 420 may be configuredfor acquiring a PPG signal or acquiring information including a PPGsignal. In some embodiments, the second acquisition unit 420 may includea blood oxygen monitor (not shown in FIG. 4). The blood oxygen monitor(not shown in FIG. 4) may be configured for acquiring the subject'sblood oxygen information using a photoelectric sensor. Blood oxygeninformation may be estimated based on two or more PPG signals. In someembodiments, at least one of the acquired PPG signals together with anECG signal may be used for calculating physiological features, which maybe used to estimate a blood pressure value based on a personalizedmodel.

In some embodiments, the blood oxygen monitor (not shown in FIG. 4) mayinclude a single photoelectric sensor, or a sensor array including aplurality of photoelectric sensors. A photoelectric sensor may includeone or more emitting ends and one or more receiving ends. The emittingend may include one or more light sources. A light source may emit oneor more of ultrasound, radio, microwave, millimeter wave, infrared,visible, ultraviolet, gamma ray, or X-ray electromagnetic radiation. Asused herein, light may also include any wavelength within the radio,microwave, infrared (IR), visible, ultraviolet (UV), or X-ray spectra,and that any suitable wavelength of electromagnetic radiation may beappropriate for use with the system, device, or apparatus disclosedherein. Merely by way of example, an emitting end may include two lightsources, a red light emitting light source such as a red light emittingdiode (LED), and an IR light emitting light source such as IR LED; theemitting end may emit light into the tissue of a subject at thewavelengths used to calculate a physiological feature of interest of thesubject (e.g., blood oxygen information). As used herein, for brevity, aspecific wavelength may also include wavelengths within a range of thespecific wavelength. For instance, the red wavelength may be betweenapproximately 600 nm and approximately 700 nm, and the IR wavelength maybe between approximately 800 nm and approximately 700 nm. In embodimentswhere a sensor array is used, each sensor may emit a single wavelength.The receiving end may be used for receiving signals resulting from theemitted lights through the subject. In some embodiments, the secondacquisition unit 420 may be configured for acquiring the subject's PPGsignals from multiple body locations (for example, the head, the neck,the chest, the abdomen, the upper arm, the wrist, the waist, the upperleg, the knee, the ankle, or the like, or a combination thereof). Insome embodiments, one or more photoelectric sensors may be placed on anyone of the multiple body locations. In some embodiments, one or morephotoelectric sensor arrays may be placed on any of the multiple bodylocations.

In some embodiments, the second acquisition unit 420 may include acontrol unit (not shown in FIG. 4) and/or a storage unit (not shown inFIG. 4). Similarly the control unit (not shown in FIG. 4) may beconfigured for controlling the acquisition process of the second signalor second information. The storage unit (not shown in FIG. 4) may beconfigured for storing the acquired signals and/or information.

The information acquisition module 210 may include one or more otheracquisition units (not shown in FIG. 4). For example, an acquisitionunit may be configured for acquiring basic information relating to thesubject, for example, the height, the weight, the age (or the date ofbirth), the gender, the arm length, the nationality, the occupation, ahabit (for example, a health-related habit such as an exercise habit),the education background, a hobby, the marital status, religious belief,a health-related history (for example, whether a subject has a historyof smoking, a food allergy, a drug allergy, a medical treatment history,a family health history, a history of genetic disease, informationregarding a prior surgery, or the like, or a combination thereof),contact information, emergency contact, or the like, or a combinationthereof. The basic information relating to the subject may be providedby the subject, a user other than the subject, or a third party (forexample, a doctor, a healthcare worker, a medical institution, aresearch facility, a peripheral device of the subject or a userwell-connected to the subject, or the like).

In another example, an acquisition unit may be configured for acquiringenvironmental information surrounding the subject, includingtemperature, humidity, air pressure, an air flow rate, an ambient lightintensity, or the like, or a combination thereof. The environmentalinformation may be acquired in a real time mode (for example, at oraround the acquisition time), or may be acquired at a certain timeinterval (for example, independent of the acquisition time).

In a further example, one or more acquisition units may be configuredfor acquiring the subject's EMG signals by way of a pressure sensingmethod, body temperature data by way of a temperature sensing method, orthe like, or a combination thereof. In a further example, an acquisitionunit may be configured for acquiring BCG signals, blood densityinformation, pH value information of the blood, or the like, or acombination thereof.

The one or more acquisition units may communicate with one or moresensors to acquire information sensed, detected or measured by the oneor more sensors. Exemplary sensors include an electrode sensor, anoptical sensor, a photoelectric sensor, a conductance sensor, a pressuresensor, an accelerometer, a gravity sensor, a temperature sensor, amoisture sensor, or the like, or a combination thereof.

Merely by way of example, an optical sensor may include an integratedphotodetector and a light source. The optical sensor may also include anamplifier. The light source may emit radiation of wavelengths of, forexample, the visible spectrum, the infrared region, or the like, or acombination thereof. The photodetector may detect the radiationresulting from light (of a wavelength, or within a range of thewavelength) that impinges upon or into and/or is reflected by a tissue,and reaches the photodetector (or referred to as the reflectedradiation). The optical sensor may be placed at a body location of asubject to detect a pulse-related signal of a subject. For instance, theoptical sensor may be a PPG sensor. In some embodiments, an opticalsensor may include a plurality of light sources, in which a light sourcemay emit light of a wavelength, or within a range of the wavelength.Thus, the plurality of light sources may emit light of variouswavelengths, or within a respective range thereof. For instance, thelight sources may emit a red light and an infrared light. In someembodiments, an optical sensor may include a plurality ofphotodetectors, in which a photodetector may be used to detect thereflected radiation resulting from the light of a wavelength, or withina range of the wavelength. In some embodiments, a photodetector may beused to detect the reflected radiation resulting from the emitted lightof various wavelengths, or within a respective range thereof. Forinstance, a photodetector may be used to detect the reflected radiationresulting from the red light and the infrared light.

In some embodiments, a plurality of PPG sensors may be assembled intoone device. One PPG sensor of the plurality of PPG sensors may include alight source, and a photodetector; the light source may emit light of awavelength, or within a range thereof; the photodetector may be used todetect the reflected radiation resulting from the emitted light (of awavelength, or within a range of the wavelength). The plurality of PPGsensors may include a PPG sensor that includes a red light emittinglight source and a photodetector that may detect the reflected radiationresulting from the red light, and a PPG sensor that includes an infraredlight emitting light source and a photodetector that may detect thereflected radiation resulting from the infrared light. In someembodiments, at least two of the plurality of PPG sensors may be placedat different locations on the body of a subject. For instance, one PPGsensor may be placed on an upper arm of the subject, and another PPGsensor may be placed on a finger of the subject. In some embodiments, atleast two of the plurality of PPG sensors may be placed at or around thesame location on the body of a subject. For instance, two PPG sensorsmay be placed at an upper arm of the subject. In another example, twoPPG sensors may be placed at a finger of the subject. In someembodiments, a device may include a PPG sensor; the PPG sensor mayinclude a plurality of light sources and a photodetector; the lightsources may emit light of various wavelengths, or within a respectiverange thereof; the photodetector may be used to detect the reflectedradiation resulting from the emitted light of various wavelengths, orwithin a respective range thereof.

The device may be a wearable or portable device including, for example,a T-shirt, a smart watch, a wristband, or the like, or a combinationthereof. The device may further include one or more processors orprocessing units. The processor or the processing unit may be configuredfor controlling the process of information acquisition, or may beconfigured for performing one or more operations of any of the modules.Signals or data may be transmitted between sensors placed at differentlocations. The transmission may be via a wireless connection (forexample, WiFi, blue tooth, near-field communication (NFC), or the like,or a combination thereof), a wired connection, or the like, or acombination thereof. For example, signals received by the sensors may betransmitted through a wireless body sensor network (BSN) or anintra-body communication (IBC).

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, theacquisition units may be integrated into an independent unit configuredfor acquiring more than one information or signal relating to thesubject. At least some of the acquisition units may be integrated intoone or more independent units. The one or more acquisition units mayshare a common control unit (not shown in FIG. 4) and/or a commonstorage unit (not shown in FIG. 4).

FIG. 5 is a block diagram illustrating an architecture of an analysismodule according to some embodiments of the present disclosure. Theanalysis module 220 may be connected to or otherwise communicate with,e.g., the peripheral equipment 240, and the server 120 through thenetwork 150. The analysis module 220 may estimate or calculate bloodpressure relating to a subject based on acquired information. Theanalysis module 220 may include a pre-processing unit 510, a featurerecognition unit 520, a calculation unit 530, and possibly a calibrationunit 540.

The pre-processing unit 510 may be configured for pre-processing theacquired information. The pre-processing may be performed to reduce orremove noise or errors in the original signals. In some embodiments, acorrection of standard deviation for the PPG waveforms may be performedby the pre-processing unit 510. For example, for a PPG waveformconsisting of tens of heart beating period, the average, median value,and/or the standard deviation of the maximum/minimum value of the PPGwaveform within each heart beating period may be calculated. A thresholdmay be specified to designate the outliers within the PPG waveforms. Forexample, a threshold of value 0.1 may be set. If the standard deviationof the maximum/minimum value of the PPG waveform within each heartbeating period is less than the threshold, then the PPG waveform may belabelled as outliers and disposed of. Similarly, the pre-processing unit510 may process the personal data of the subject. A trusted interval forthe values of the personal data may be set. Any personal data outside ofthe trusted interval may be labelled as questionable and needs to becorrected. For example, if the height of the subject is 5 cm, and/or theweight of the subject is 5 kilograms, then the personal data of thesubject may be labelled as questionable and needs to be corrected.Exemplary methods for pre-treatment may include low-pass filtering,band-pass filtering, wavelet transform, median filtering, morphologicalfiltering, curve fitting, Hilbert-Huang transform, or the like, or anycombination thereof. Descriptions regarding methods and systems forreducing or removing noise from a physiological signal, e.g., a PPGsignal or an ECG signal, may be found in, e.g., International PatentApplication Nos. PCT/CN2015/077026 filed Apr. 20, 2015,PCT/CN2015/077025 filed Apr. 20, 2015, and PCT/CN2015/079956 filed May27, 2015, each of which is incorporated by reference.

In some embodiments, the physiological features obtained in the featurerecognition unit 520 may be transferred to the pre-processing unit 510for treatment of outliers. For example, a PPG waveform of a subject maybe designated as training data. A collection of training data may bestored in the database 73. The physiological features of the PPGwaveform may be obtained in the feature recognition unit 520 andtransferred to the pre-processing unit 510. The pre-processing unit 510may calculate Cook's Distance for the physiological features of the PPGwaveform. If the Cook's Distance is larger than C/N, then the PPGwaveform as training data may be disposed of. Here N is the number oftraining data, C is a pre-determined value. In some embodiments, C maybe chosen as an integer larger or equal to 4.

The pre-processing unit 510 may include one or more pre-processingsub-units (not shown in FIG. 6). The pre-processing sub-units may (notshown in FIG. 6) perform one or more pre-processing steps forpre-processing the acquired signals in series (e.g., a pre-treatmentstep performed after another pre-treatment step has commenced orcompleted) or in parallel (e.g., some pre-treatment steps performed ator around the same time). The pre-treatment unit 510 may control orcoordinate the operations of the pre-processing sub-units (not shown inFIG. 6). The control or coordination may be performed by, e.g., acontroller within the pre-processing unit 510 (not shown in FIG. 6) or acontroller outside of the pre-processing unit 510. The pre-processingsub-units may be arranged in series or in parallel.

This description is intended to be illustrative, and not to limit thescope of the claims. Many alternatives, modifications, and variationswill be apparent to those skilled in the art. The features, structures,methods, and other characteristics of the exemplary embodimentsdescribed herein may be combined in various ways to obtain additionaland/or alternative exemplary embodiments. For example, the pre-treatmentsub-units may be combined variously in order to achieve betterpre-treatment effect. It should be noted that the pre-treatmentsub-units are not necessary for the function of the system. Similarmodifications should fall within the metes and bounds of the claims.

The recognition unit 520 is configured for analyzing the acquiredinformation to recognize or identify a feature. In some embodiments, theacquired information may have been pre-processed before it is processedin the recognition unit 520. In the exemplary context of blood pressuremonitoring, the acquired information may include a PPG signal, an ECGsignal, a BCG signal, or the like, or a combination thereof; exemplaryfeatures of the acquired information may include characteristic points,peak points, valley points, amplitude, time intervals, phase,frequencies, cycles, ratio, maximum slope, starting time, ending time,direct current (DC) component, alternating current (AC) component, orthe like, or any combination thereof, of a function of the PPG waveform.The function of the waveform may be identical function, or the firstderivative or higher order derivatives of the waveform.

The recognition unit 520 may be configured for analyzing different typesof information or different portions of information. The analysis may beperformed by, e.g., one or more recognition sub-units (not shown in FIG.6). For example, the acquired information includes various types ofphysiological signals (e.g., a PPG signal and an ECG signal) and may beanalyzed by different recognition sub-units. Exemplary methods that maybe employed in the recognition unit 520 may include a threshold method,a syntactic approach of pattern recognition, Gaussian functiondepression, wavelet transform, a QRS complex detection, a lineardiscriminant analysis, a quadratic discriminatory analysis, a decisiontree, a decision table, a near neighbor classification, a wavelet neuralnetworks model, a support vector machine, gene expression programming,hierarchical clustering, a mean cluster analysis, a Bayesian networkmodel, a principal component analysis, a Kalman filter, Gaussianregression, linear regression, Hidden Markov Model, association rules,an inductive logic method, or the like, or any combination thereof.Various methods may be used in parallel or may be used in combination.Merely by way of example, the recognition unit may use two differentmethods when processing two types of signals. As another example, therecognition unit may use two different methods, e.g., one method afteranother, when processing one type of signal.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. Merely by way ofexample, the analyzed features may be uploaded to the public clouds orthe personal clouds and may be used in subsequent calculation orcalibration. As another example, the recognition sub-units (not shown inFIG. 6) are not necessary for the function of the system. Similarmodifications should fall within the metes and bounds of the presentdisclosure.

The calculation unit 530 may be configured for performing variouscalculations to determine, e.g., coefficients of a model or functionrelating to a physiological feature of interest, mean, median value,and/or standard deviation of calculated blood pressure, or the like, ora combination thereof. In some embodiments, the calculation unit 530 maytry to reduce the number of physiological features based on apersonalized model for a subject. For example, the calculation unit 530may calculate the Akaike information criterion (AIC) value based on thephysiological features and the model selected for the subject. If theAIC value decreases after a physiological feature F1 is removed from theset of physiological features, then the physiological feature F1 may bedisposed of from the set of physiological features. The process ofdisposing of physiological features may be stopped when the AIC valueincreases. The physiological features left after the process ofdisposing of physiological features may be referred to as “effectivephysiological features”. The calculation unit 530 may include one ormore calculation sub-units (not shown in FIG. 6) to perform thecalculations. A physiological feature of interest may including, e.g.,PTT, PTTV (pulse transit time variation), a BP, a SBP, a DBP, a pulserate, a heart rate, a HRV, cardiac murmur, blood oxygen saturation, ablood density, a blood oxygen level, or the like, or any combinationthereof.

Exemplary methods that may be employed in the calculation unit 530 mayinclude a direct mathematical calculation, an indirect mathematicalcalculation, a compensated calculation, a vector operation, a functionoperation, a wave speed evaluation, an equation feature evaluation, atension evaluation, or the like, or any combination thereof. One or morecalculation models may be integrated in the calculation sub-units, orthe calculation models may be placed in the server 120, or thecalculation models may be placed in public clouds. Different models maybe loaded when different coefficients or physiological features are tobe calculated. For example, a linear calculation model in a calculationsub-unit may be used for calculating the SBP, while another non-linearcalculation model in another calculation sub-unit may be used forcalculating the DBP. An initial data or intermediate result used forcalculating a physiological feature of interest may be retrieved orloaded from the information acquisition module 210, the analysis module220, the server 120, the external data source 130, the peripheralequipment 240, or the like, or any combination thereof. The externaldata source 130 may include information from medical institution 131,research facility 132, database 133, and peripheral device 134. Theinitial data and the intermediate result may be combined in various waysin the calculation unit 530.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. In one embodiment,calculated coefficients or calculated physiological features may be usedas an intermediate result for further analysis. In another example, anindividual physiological feature of interest or one group of relatedphysiological features of interest may be calculated by the calculationunit.

The calibration unit 540 may be configured for performing a calibration.The calibration (also referred to as calibration process or calibrationprocedure) may include one or more steps of retrieving calibration data(or calibration values) for a subject; acquiring a set of information ofthe subject using a device to be calibrated or used in a future process(e.g., a wearable or portable device); determining a calibrated model ora portion thereof for the calibrated device with respect to the subject,or the like, or a combination thereof. The acquired set of informationmay include information provided by the subject or a user other than thesubject, or information acquired by using the device to be calibrated,or the like, or a combination thereof. A set of calibration data mayinclude a specific physiological feature of interest obtained in onecalibration process, an acquired set of information relating to thespecific physiological feature of interest in the same calibrationprocess.

Merely by way of example, the device to be calibrated may calculateblood pressure (including the SBP and the DBP) based on personalizedmodel selected for a subject and effective physiological features. Insome embodiments, the device to be calibrated may be a portion of thesystem other than the calibration unit 540. A set of calibration datamay include a SBP and a DBP, both measured by a healthcare provider in ahospital setting, and a corresponding ECG waveform and a correspondingPPG waveform acquired using the device to be calibrated. Thecorresponding ECG waveform and the corresponding PPG waveform acquiredusing the device to be calibrated may correspond to the SBP and the DBPmeasured by a healthcare provider. The corresponding ECG waveform andthe corresponding PPG waveform may be acquired using the device to becalibrated at or around the time the SBP and the DBP are measured by ahealthcare provider.

In some embodiments, a set of calibration data may include a SBP, a DBP,and a corresponding ECG waveform and a corresponding PPG waveform, allacquired using the device to be calibrated. For instance, thecalibration unit 540 may include or communicate with a cuff-based bloodpressure monitor. The cuff-based blood pressure monitor may beintegrated into the system or device, or a portion thereof (e.g., thecalculation unit, the information acquisition module, or the like). Forinstance, a cuff-based blood pressure monitor, an ECG monitor that mayacquire ECG information, and one or more PPG sensors may be packagedinto a device, or a system, or a portion thereof. The cuff-based bloodpressure monitor may measure a SBP and a DBP at a certain time interval(e.g., 15 min, 30 min, 1 hour, 2 hour, a day, or the like). The set ofcalibration data may be acquired automatically based on a defaultsetting of the system, or preset instructions by the subject or a userother than the subject (also referred to as a third party). Exemplarythird parties may be a doctor, a healthcare worker, a medicalinstitution, a research facility, a peripheral device of the subject ora user well-connected to the subject, or the like. The set ofcalibration data acquired by the calibration unit 540 may be transmittedto the calculation unit 530 or other modules or units in real time. Theset of calibration data may be stored in a storage device disclosedanywhere in the present disclosure or may be stored in the server 120.If needed, the set of calibration data may be loaded from the storagedevice or the server 120 automatically.

One or more sets of calibration data may be used to determinecoefficients of a calibrated model, or some other portion(s) of thecalibrated model. The calibrated model may be used in a future processfor calculating the physiological feature of interest based on anotherset of information that is acquired using the calibrated device. In afuture process, the calibrated device may acquire a set of informationthat is the same or similar to the set of information acquired for thecalibration. For instance, the other set of information may includeinformation acquired using the same device as that used in thecalibration (e.g., the device including one or more sensors),information of the same type as that acquired in the calibration (e.g.,the age of the subject, the acquisition time during the day, thephysiological or psychological condition of the subject, or the like, ora combination thereof), or the like, or a combination thereof. Thecalibrated model may be used to calculate or estimate the physiologicalfeature of interest accordingly. Exemplary methods that may be used inthe calibration to obtain the calibrated model may include a regressionanalysis, a linear analysis, a functional operation, reconstitution,Fourier transform, Laplace transform, or the like, or a combinationthereof.

In a calibration process, a set of calibration data may include aspecific physiological feature of interest obtained based on ameasurement using one or more devices other than the device to becalibrated. Merely by way of example, the specific physiological featureof interest may be obtained based on a measurement performed on thesubject by the calibration unit 540 (e.g., a cuff-based blood monitor).As another example, the specific physiological feature of interest maybe obtained based on a measurement performed on the subject by ahealthcare professional in a hospital or a doctor's office. As anotherexample, the specific physiological feature of interest may be obtainedbased on a measurement performed on the subject by the subject orsomeone else using a clinical device or a household device. Forinstance, the physiological feature of interest may be measured using adevice including, e.g., an auscultatory device, an oscillometric device,an ECG management device, a PPG management device, or the like, or anycombination thereof.

In a calibration process, a set of calibration data may include aspecific physiological feature of interest previously calculated orestimated by the system or a portion of the system. Merely by way ofexample, the physiological feature of interest calculated by the systembased on a set of acquired information and a calibrated function in thesystem may be used in a next calibration to update or generate acalibrated model, and the updated calibrated model may be used in thefuture to calculate the physiological feature of interest (the firstaspect of the calibration process described above). As another example,the physiological feature of interest calculated by the system based ona set of acquired information and a calibrated function in the systemmay be used in a next measurement for the physiological feature ofinterest (the second aspect of the calibration process described above).The calculated physiological feature of interest of the subject may bestored in a storage device disclosed anywhere in the present disclosureor in the server 120, for future use in connection with the subject orother subjects.

In the exemplary context of estimating BP of a subject (including SBPand DBP), based on PTT, the correlation between BP and PTT may berepresented by a model including mathematical processing, and a factoredfunction, while the factored function may include a function (ƒ) andcoefficient (B). As used herein, a calibration may include at least twoaspects. A first aspect is that a model is determined based on one ormore sets of calibration data (or calibration values). The determinedmodel may be referred to as a calibrated model. To use the calibratedmodel in a specific measurement, signals need to be acquired to providePTT, and a set of calibration data including PTT0, SBP0, and DBP0. Thecorrelation between BP and PTT may depend on other elements, in additionto PTT. Merely by way of example, the correlation between BP and PTT maydepend on HRV, PTTV, in addition to PTT. To use the calibrated model ina specific measurement, signals need to be acquired to provide PTT, HRV,and PTTV, and a set of calibration data including PTT0, SBP0, DBP0,HRV0, and PTTV0.

The first aspect of calibration may be performed using personalizedcalibration data relating to the subject, or peer data, or empiricaldata. This aspect of calibration may be performed real time when aspecific measurement is performed. A model to be used to estimate BPbased on the PTT in the specific measure may be derived based on one ormore sets of calibration data. The selection of the one or more sets ofcalibration data may be based on the PTT in the specific measurement.This aspect of calibration may be perform offline, independent of aspecific measurement.

A second aspect of the calibration includes acquiring a set ofcalibration data to be applied in a calibrated model so that a bloodpressure may be estimated based on PTT acquired in a specificmeasurement, according to the model and the set of calibration data. Insome embodiments, the set of calibration data to be used in the specificmeasurement may be selected from, e.g., a plurality of sets ofcalibration data. The plurality of sets of calibration data may includepersonalized data relating to the subject, peer data, or empirical data.The plurality of sets of calibration data may be saved in the system,e.g., in the library 900 (see FIG. 1). The plurality of sets ofcalibration data may be saved in a server that is part of or accessiblefrom the system. In some embodiments, the set of calibration data may beselected based on the PTT in the specific measurement.

A calibrated model to be used for a specific subject may be based on thecalibration data of the same subject. A calibrated model to be used fora specific subject may be based on a combination of the calibration dataof the same subject and calibration data from a group of subjects (e.g.,peer data discussed elsewhere in the present disclosure). A calibratedmodel to be used for a specific subject may be based on the calibrationdata from a group of subjects (e.g., peer data or empirical datadiscussed elsewhere in the present disclosure). The specific subject maybe included in the group, or not included. The calibration data may bestored in a storage device disclosed anywhere in the present disclosureor the server 120, or the like, or a combination thereof. Personalizedcalibration data of different subjects may be stored in correspondingpersonal accounts of respective subjects in the server 120 or a personalcloud. Calibration data from various subjects may be stored in anon-personalized database for future use. For instance, calibration datafrom various subjects may be divided based on one or morecharacteristics of the respective subjects. Exemplary characters mayinclude, e.g., age, gender, stature, weight, a body fat percentage,color of skin, a family health history, a life style, an exercise habitor other habit, diet, a psychological condition, a health condition, aneducation history, occupation, or the like, or a combination thereof. Insome embodiments, a portion of the calibration data (e.g., peer datadiscussed elsewhere in the present disclosure) so divided may be usedfor calibration purposes by a group of subjects that share the same orsimilar characteristic(s).

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, astorage unit (not shown in FIG. 6) may be added to the calibration unit540 or the calculation unit 530, or a combination thereof. The storageunit in the calibration unit 540 may store the calibration data orhistorical data relating to calibration process. The storage unitrelating to calculation unit 530 may store calculation models or datarelating to calculation process. Additionally, peer data may be used asinitial data or an intermediate result during calibrating.

The analysis module 220 may be implemented on one or more processors.Various units of the analysis module 220 may be implemented on one ormore processors. For example, the pre-treatment unit 510, therecognition unit 520, the calculation unit 530, and the calibration unit540 may be implemented on one or more processors. The one or moreprocessors may transmit signals or data with a storage device (not shownin FIG. 6), the information acquisition modules 1, 2, and 3, theperipheral equipment 240, and the server 120. The one or more processorsmay retrieve or load signals, information, or instructions from thestorage device (not shown in FIG. 6), the information acquisitionmodules 1, 2, and 3, the peripheral equipment 240, or the server 120,and process the signals, information, data, or instructions, or acombination thereof, to perform pre-treatment, calculation of one ormore physiological features of interest, calibration, or the like, or acombination thereof. The one or more processors may also be connected orcommunicate with other devices relating to the system 100, and transmitor share signals, information, instructions, the physiological featuresof interest, or the like with such other devices via, e.g., a mobilephone APP, a local or remote terminal, or the like, or a combinationthereof.

FIG. 6 is a flowchart diagram of an exemplary process for estimatingblood pressure according to some embodiments of the present disclosure.Beginning in step 610, information including a first signal and a secondsignal may be acquired. For example, the first signal may be a ECGsignal, and the second signal may be a PPG signal. The first signal andsecond signal may be related to a subject. The acquisition of thesignals may be performed by the information acquisition module 210. Insome embodiments, the first and second signals may be acquiredsimultaneously, at or around the same time. In some embodiments, onesignal may be acquired prior to the other signal. In some embodiments,information including or relating to the first signal or the secondsignal may be acquired in step 610. For instance, information aboutpersonal data of the subject, such as the age, weight, height, andhistorical medical record, may be acquired. As another example, basicinformation relating to the subject and/or environmental information maybe acquired.

Merely by way of example, the first signal or the second signal may bephysiological signals, e.g., an electrocardiogram (ECG) signal, apulse-wave-related signal (such as photoplethysmogram (PPG)), aphonocardiogram (PCG) signal, an impedance cardiogram (ICG) signal, orthe like, or any combination thereof. In some embodiments, the firstsignal and the second signal may be of different types. For example, thefirst and second signals may be the combination of an ECG signal and aPPG signal, the combination of an ECG signal and a PCG signal, thecombination of an ECG signal and an ICG signal, or the like. In someembodiments, the first signal and the second signal may be of the sametype. For example, the first and second signals may be two PPG signalsthat may be detected at different locations on the body of the subject.The exemplary locations on the body of the subject may include, e.g.,the finger, the radial artery, the ear, the wrist, the toe, or thelocations that are more suitable for ambulatory monitoring in currentsensor designs.

In step 620, at least some of the acquired information may bepre-processed. In some embodiments, the acquired first and secondsignals may be pre-processed. The pre-processing may be performed toreduce or remove noise or errors in the signals or signal related data.Exemplary methods that may be used in the pre-treatment may includelow-pass filtering, band-pass filtering, wavelet transform, medianfiltering, morphological filtering, curve fitting, Hilbert-Huangtransform, or the like, or any combination thereof. During the processof the pre-processing, the methods mentioned herein may be used inparallel or may be used in combination. Descriptions regarding methodsand systems for reducing or removing noise from a physiological signal,e.g., a PPG signal or an ECG signal, may be found in, e.g.,International Patent Application Nos. PCT/CN2015/077026 filed Apr. 20,2015, PCT/CN2015/077025 filed Apr. 20, 2015, and PCT/CN2015/079956 filedMay 27, 2015, each of which is incorporated by reference. Additionally,real-time transformation of time domain or frequency domain may also beimplemented in step 820, and the signals or related information may beused in time domain, frequency domain, wavelet domain, or all of them.

In step 630, the features of the first and second signals may berecognized or identified. In the exemplary context of blood pressuremonitoring, the first signal or the second signal may include a PPGsignal, an ECG signal, a BCG signal, or the like; exemplary features ofthe first signal or the second signal may include characteristic points,peak points, valley points, amplitude, time intervals, phase,frequencies, cycles, ratio, maximum slope, starting time, ending time,direct current (DC) component, alternating current (AC) component, orthe like, or any combination thereof, of a function of the PPG waveform.The function of the waveform may be identical function, or the firstderivative or higher order derivatives of the waveform. For example, onecharacteristic point may be a peak or a valley of the first signal,e.g., the peak or valley of R wave of an ECG signal, a fastest risingpoint of a PPG signal, a higher order moment or a higher orderderivative of the PPG signal, a pulse area of the PPG signal, a maximumpositive peak of S2 of a PCG signal, or a peak of an ICG signal, or thelike.

In step 640, a dataset including the physiological features identifiedand the personal data of the subject may be cleaned. By cleaning thedataset we mean the outliers within the dataset may be removed orcorrected. For example, for a PPG waveform consisting of tens of heartbeating period, the average, median value, and/or the standard deviationof the maximum/minimum value of the PPG waveform within each heartbeating period may be calculated. A threshold may be specified todesignate the outliers within the PPG waveforms. If the standarddeviation of the maximum/minimum value of the PPG waveform within eachheart beating period is less than the threshold, then the PPG waveformmay be labelled as outliers and disposed of. Similarly, the personaldata of the subject may be laundered. A trusted interval for the valuesof the personal data may be set. Any personal data outside of thetrusted interval may be labelled as questionable and needs to becorrected. For example, if the height of the subject is 5 cm, and/or theweight of the subject is 5 kilograms, then the personal data of thesubject may be labelled as questionable and needs to be corrected.

A pre-treatment step may be performed to assess an acquired signal (forexample, an ECG signal, a PPG signal, etc.) before one or more featuresof the signal is identified. For instance, an acquired ECG signal may beaccessed before one or more features of the signal is identified. Theassessment may be performed to evaluate whether a valid ECG signal isacquired. The assessment may be performed by way of, for example, apattern recognition process. For instance, the R peak of an acquired ECGsignal may be determined by the pattern recognition process. In someembodiments, the system may identify an abnormal signal or waveform(e.g., an abnormal sinus rhythm R wave, another physiological signal, orthe like) that may be unsuitable for deriving physiological features;such an abnormal signal or waveform may be abandoned to avoid to beinvolved in the subsequent calculation or analysis. In some embodiments,the acquired ECG signal may be compared with a reference signal todetermine whether the acquired ECG signal includes an abnormal R wave.The reference signal may be a normal sinus rhythm ECG signal, or may beretrieved from a database having historical data.

The ECG waveform and the PPG waveform are cyclical signals, i.e. thecharacteristic points occur substantially cyclically or periodically.Thus, during recognition of characteristic points of the PPG waveform, athreshold may be set regarding the time window or segment within whichthe characteristic points on the PPG waveform may be identified and usedto determine physiological features. In one example, the time window maybe tens of heart beating periods. Merely by way of example, an analysisto identify a fiduciary point on a PPG waveform is performed on asegment of the PPG waveform occurring within 2 seconds from the timepoint when the maximum point on the ECG waveform is identified, in orderto obtain physiological features. As another example, an analysis toidentify a fiduciary point on a PPG waveform is performed on a segmentof the PPG waveform occurring between two consecutive peak points on theECG waveform, in order to approximate the PTT. As a further example, thetime window may be set based on the heart rate of the subject. Forinstance, the time window may be set based on the heart rate of thesubject at or around the acquisition time, or an average heart rate ofthe subject for a period of time, or an average heart rate of a group ofpeople (for example, a sub-group of people who share a same or similarcharacteristic with the subject; exemplary characteristic may includeage, gender, nation, stature, weight, a body fat percentage, color ofskin, a family health history, a life style, an exercise habit or otherhabit, diet, occupation, illness history, education background, maritalstatus, religious belief, or the like, or any combination thereof.

The cycle of ECG or the cycle of PPG may vary. As an example, the cycleof ECG or the cycle of PPG of different subjects may be different. Asanother example, the cycle of ECG or PPG of the same subject may varyunder different situations (e.g., when the subject is exercising orasleep, at different times of a day, at the same or similar time ondifferent days), or the like, or a combination thereof. In one example,the time window threshold may be set based on the heart rate of asubject (for example, the cycle of average person is approximately60-120 beats per minute). The heart rate may be an average value over aperiod of time (e.g., a week, a month, a year, or the like). The heartrate may be one measured at or around the acquisition time. The heartrate may be measured based on, e.g., the ECG signal, the PPG signal, orthe like. The time window may be set or updated based on the measuredheart rate. In another example, the time window may be set by, e.g., thesystem, the subject, or a user other than the subject, based on thephysiological information of the subject. For example, the physiologicalinformation may include motion or not, taking medicine or not, good orbad mood, emotional stress or not, or the like, or a combinationthereof. In another example, the time window may be a fixed valuedefined by the system, the subject, or a user other than the subject(e.g., his doctor, health care provider, or the like).

In step 650, a plurality of models and cost functions may be obtained.The models to select may be one of the following forms:

=g _(sbp)(X ₁ ,X ₂ , . . . ,X _(k))+R(id)

=g _(dbp)(X ₁ ,X ₂ , . . . ,X _(k))+R(id)

=g _(bpdiff)(X ₁ ,X ₂ , . . . ,X _(k))+R(id)

=g _(lnsbp)(X ₁ ,X ₂ , . . . ,X _(k))+R(id)

=g _(lndbp)(X ₁ ,X ₂ , . . . ,X _(k))+R(id)

Where Sbp is the systolic pressure, Dbp is the diastolic pressure.g_(sbp)(⋅), g_(dbp)(⋅), g_(bpdiff)(⋅), g_(lnsbp)(⋅), g_(lndbp)(⋅) arethe structural components (non-random) of the predictive functions ofthe input physiological features, such as characteristic points of thePPG signals, demographic information of the subject. The functionalrelationship can be linear, non-linear, regression tree or random forestwhich is determined by the cost function. An example of linear functiontakes the following form: g(X₁, X₂, . . . )=β₀+β₁X₁+β₂X₂ . . . βi is themodel coefficient to be determined, x_(i) is the effective physiologicalfeatures, i=1, 2, . . . M, where M is the number of chosen physiologicalfeatures, id is the personal variable related to the subject. Thestructural component g_(sbp)(⋅), g_(dbp)(⋅), g_(bpdiff)(⋅),g_(lnsbp)(⋅), g_(lndbp)(⋅) are set to be constant across generalpopulation, while R(id) denotes the random component of the predictivefunction, which is individual specific. In some embodiments, thestructural component g_(sbp)(⋅), g_(dbp)(⋅), g_(bpdiff)(⋅),g_(lnsbp)(⋅), g_(lndbp)(⋅) may be dependent upon the training dataand/or the calibration data. The cost functions to select may be mean,median, standard deviation of the prediction errors of the test data.

In step 660, feature extraction for each model may be performed vialikelihood-based principles such as the Akaike information criterion(AIC), the Bayesian information criterion (BIC), via Cross-validationmethods, or via shrinkage-based methods. An optimal subset of thephysiological features that will lead to minimal cross-validatedprediction errors can be determined using the above methods. As a datadriven approach, the elements of the optimal subset can vary dependingon population (such as gender and/or age), blood pressure measuringpositions, etc.

In step 670, a personalized model may be determined based on a costfunction and effective physiological features obtained in step 660. Forexample, the cost function may be chosen as the standard deviation ofprediction error of the test data. Based on the behavior of the modelslisted in step 650, a model with minimum standard deviation of theprediction errors of the test data may be designated as the personalizedmodel for the subject.

In step 680, BP (blood pressure) values of the subject may be calculatedbased on the personalized models and effective physiological features,e.g., maximum and minimum value of the slope of the PPG pulse wave, DC(direct current) component of the PPG pulse wave, AC (alternatingcurrent) component of the PPG pulse wave, the determined PTT (pulsetransit time), PTTV (pulse transit time variation) and HRV, or the like,or a combination thereof. The personalized model may include a linearfunction based model, a nonlinear function based model, a regressiontree/random forest based model.

While the foregoing has described what are considered to constitute thepresent disclosure and/or other examples, it is understood that variousmodifications may be made thereto and that the subject matter disclosedherein may be implemented in various forms and examples, and that thedisclosure may be applied in numerous applications, only some of whichhave been described herein. Those skilled in the art will recognize thatpresent disclosure are amenable to a variety of modifications and/orenhancements. For example, the pre-treatment step 530 may not benecessary. Additionally, a third signal may be acquired if needed, andthe third signal may be a signal with the same type with the firstsignal or the second signal, or may be a signal different with the firstsignal or the second signal.

FIG. 7 illustrates an exemplary monitoring device 700 according to someembodiments of the present disclosure. The monitoring device 700 mayinclude a measurement module 710, an electrode 720, a finger clip 730,and/or a terminal 740. The monitoring device 700 may be connected orotherwise communicate with the terminal 740.

The measurement module 710 may be configured for acquiring information,for example, an ECG signal, a PPG signal, blood oxygen information, orthe like, or a combination thereof. The measurement module 710 also maybe configured for analyzing and processing the acquired information, ordetermining or estimating a physiological feature of interest, ordetermining blood pressure, or the like.

According to the embodiment, the measurement module 710 includes an ECGacquisition unit configured for acquiring ECG signals by way of electricsensing method, and a PPG signal acquisition unit configured foracquiring PPG signal related information by way of photoelectric sensingmethod. The acquired signals or information may be stored in the server120, or a storage device (not shown in FIG. 7) integrated in themeasurement module 710, or any storage device disclosed anywhere in thepresent disclosure.

The monitoring device 700 may be a wearable device, a portable device, amedical monitoring device in hospital, or health-care monitoring deviceat home, or the like. It may be seen that a plurality of electrodes 720are located on the chest of the subject and the electrodes areconfigured for recording one or more potential changes of the subject.The potential changes may constitute an ECG waveform and the ECGwaveform may be transmitted to the measurement module 710 by one or morewires. It also may be seen that one or more photoelectric sensors 730are located on the finger of the subject and the photoelectric sensorsare configured for detecting one or more PPG signals or pulse waverelated signals. The detected signals may be transmitted to themeasurement module 710 by wires or wirelessly. In this embodiment, theone or more photoelectric sensors are located on the finger of thesubject and this arrangement or locating form is only provided forillustration purposes. In one example, the one or more photoelectricsensors may be located in the upper arm of the subject.

According to the embodiment, the calibration module 740 may include acuff-based blood pressure monitor. The cuff-based blood pressure monitormay be configured for acquiring SBP and DBP values that may be used ascalibration data (e.g., SBP0, DBP0, PTT0, or the like, or a combinationthereof.) during one or more processes of the measurement module 710. Asillustrated, the cuff-based blood pressure monitor may include a cuff, apneumatic device (not shown in FIG. 7), a cable (not shown in FIG. 7), atransceiver (not shown in FIG. 7), and/or a controller (not shown inFIG. 7). The cuff may feature an internal, airtight pocket that may besecured onto a portion of a subject to deliver a pressure. For instance,the cuff may wrap around the subject's upper arm to deliver a pressure.The pneumatic device may include a pump, a valve, analog/digitalconverter, etc. During the process of acquiring calibration data, thepneumatic device may inflate the cuff and acquire a plurality of data(e.g., SBP0, DBP0, or the like, or a combination thereof.). The acquireddata may be transmitted by the cable 750 to the transceiver (not shownin FIG. 7) for subsequent process.

The acquired ECG signal, PPG signal, calibration data (e.g., SBP0, DBP0,PTT0, or the like, or a combination thereof) may be transmitted to themeasurement module 710 to be used for calculating a blood pressure valueof the subject. The calculation may be performed by the measurementmodule 710, or may be performed by an analysis module (not shown)integrated in the measurement module 710. In some embodiments, themeasurement module 710 may be a wearable or portable device separatefrom and capable of communicating with one or more photoelectric sensors730, the electrodes 720, and/or the calibration module 740, asillustrated in FIG. 7. In some embodiments, the measurement module 710may be packaged together with the calibration module 740. For instance,the measurement module 710 may be attached to the cuff of thecalibration module 740.

Before the calculation, one or more operations may be performed, forexample, pre-treatment, feature identification, feature estimation,calibration, or the like, or a combination thereof. More descriptionsregarding the analysis may be found in International Patent ApplicationNo. PCT/CN2015/083334 filed Jul. 3, 2015 and International PatentApplication No. PCT/CN/2015/096498 filed Dec. 5, 2015. The details maybe displayed in the terminal 740, or may be transmitted to a relatedthird party (for example, a medical institution). The details may bedisplayed in a display device (see FIG. 7) of the measurement module710.

The monitoring device 700 may also include one or more additionalcomponents including a WIFI device, a blue tooth device, a NFC device, aGPS device, or the like, or a combination thereof. For instance, theWIFI device may be used for linking to a wireless network. The bluetooth device may be used for data transformation among some wired orwireless terminals within a certain distance. The NFC device may be usedto enable terminals establishing radio communication within a shortdistance (10 cm or less). The GPS device may allow the subject to findhis own position, or the GPS device may be used to navigate, or thelike, or a combination thereof. The additional components may beconnected or otherwise communicate with the measurement module 710, thecalibration module 740, the terminal 740, and the server 120.

The monitoring device 700 may be used in a health care institute (e.g.,a hospital), or may be used at home. The monitoring device 700 may beused for real time physiological feature monitoring. The acquiredsignals, information, data, or calculated physiological features ofinterest may be displayed in real time in a display device (not shown)or in the terminal 740. The subject, a user other than the subject(e.g., a doctor) may review the related information anywhere andanytime. In some embodiments, if the monitoring device 700 is used athome, the monitoring device 700 may communicate with a healthcareprovider located in a location remote from the subject. Thecommunication may be achieved directly by the monitoring device 700, orindirectly via, for example, the terminal 740 carried by the subject.The physiological feature, as well as location information, of thesubject may be transmitted to the healthcare provider in real-time,periodically, or when a triggering event occurs. Exemplary triggerevents are described elsewhere in the present disclosure. When anemergency occurs, for example, the physiological feature exceeding athreshold, the healthcare provider may be notified, the subject may belocated based on the positioning information from the GPS or locationsensor, and medical services may be provided accordingly.

FIG. 8 is a flowchart diagram of an exemplary process for utilizing thedisclosed method to estimate blood pressure according to someembodiments of the present disclosure. Beginning in step 810,calibration data of a subject may be obtained. The calibration data maybe the ECG waveform and PPG waveform, together with the measured bloodpressure (including SBP and DBP) using traditional Korotkoff sounds oran oscillometric method. Calibration data of the subject may be storedin the database 133. In some embodiments, the calibration may be takenwhile the subject is standing, sitting, or lying on a bed. Thecalibration may be taken at various time during the same or differentdaytime. For example, the calibration may be taken during the morning,noon, and/or night of a day.

In step 820, information including a first signal and a second signalrelating to the subject may be acquired, together with a personalizedmodel for the subject. The personalized model may be determinedbeforehand, choosing one of the models in the models 123 as in FIG. 1.The first signal may be an ECG signal. The second signal may be a PPGsignal. In some embodiments, personal data regarding the subject mayalso be acquired in step 820.

In step 830, at least some of the acquired information may bepre-processed to dispose of the abnormal signal. For example, part ofthe PPG signals may be abnormal and need to be disposed of. In step 840,effective physiological features may be obtained using the first signal,second signal, and the personalized model for the subject. The effectivephysiological features may be used as model variables, and the personaldata of the subject may be used as personal variable id for thepersonalized model.

In step 850, blood pressure based on the effective physiologicalfeatures of the first signal and second signal may be calculated, usingthe designated personalized model and possibly personal data of thesubject. The blood pressure of the subject may be output in step 860.

FIG. 9 depicts the architecture of a mobile device that may be used torealize a specialized system implementing the present disclosure. Inthis example, the device (for example, the terminal 140) on whichinformation relating to blood pressure monitoring is presented andinteracted—with is a mobile device 900, including, but is not limitedto, a smart phone, a tablet, a music player, a handled gaming console, aglobal positioning system (GPS) receiver, and a wearable computingdevice (for example, eyeglasses, wrist watch, etc.), or in any otherform factor. The mobile device 900 in this example includes one or morecentral processing units (CPUs) 940, one or more graphic processingunits (GPUs) 930, a display 920, a memory 960, a communication platform910, such as a wireless communication module, storage 990, and one ormore input/output (I/O) devices 950. Any other suitable component,including a system bus or a controller (not shown), may also be includedin the mobile device 900. As shown in FIG. 11, a mobile operating system970, for example, iOS, Android, Windows Phone, etc., and one or moreapplications 980 may be loaded into the memory 960 from the storage 990in order to be executed by the CPU 940. The applications 980 may includea browser or any other suitable mobile apps for receiving and renderinginformation relating to blood pressure monitoring or other informationfrom the engine 200 on the mobile device 900. User interactions with theinformation stream may be achieved via the I/O devices 950 and providedto the engine 200 and/or other components of system 100, for example,via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein (for example, the engine 200, and/or other components of thesystem 100 described with respect to FIGS. 1-8). The hardware elements,operating systems and programming languages of such computers areconventional in nature, and it is presumed that those skilled in the artare adequately familiar therewith to adapt those technologies to theblood pressure monitoring as described herein. A computer with userinterface elements may be used to implement a personal computer (PC) orother type of work station or terminal device, although a computer mayalso act as a server if appropriately programmed. It is believed thatthose skilled in the art are familiar with the structure, programmingand general operation of such computer equipment and as a result thedrawings should be self-explanatory.

FIG. 10 depicts the architecture of a computing device that may be usedto realize a specialized system implementing the present disclosure.Such a specialized system incorporating the present teaching has afunctional block diagram illustration of a hardware platform thatincludes user interface elements. The computer may be a general purposecomputer or a special purpose computer. Both may be used to implement aspecialized system for the present disclosure. This computer 1000 may beused to implement any component of the blood pressure monitoring asdescribed herein. For example, the engine 200, etc., may be implementedon a computer such as computer 1000, via its hardware, software program,firmware, or a combination thereof. Although only one such computer isshown, for convenience, the computer functions relating to the bloodpressure monitoring as described herein may be implemented in adistributed fashion on a number of similar platforms, to distribute theprocessing load.

The computer 1000, for example, includes COM ports 1050 connected to andfrom a network connected thereto to facilitate data communications. Thecomputer 1000 also includes a central processing unit (CPU) 1020, in theform of one or more processors, for executing program instructions. Theexemplary computer platform includes an internal communication bus 1010,program storage and data storage of different forms, for example, disk1070, read only memory (ROM) 1030, or random access memory (RAM) 1040,for various data files to be processed and/or transmitted by thecomputer, as well as possibly program instructions to be executed by theCPU. The computer 1000 also includes an I/O component 1060, supportinginput/output between the computer and other components therein such asuser interface elements 1080. The computer 1000 may also receiveprogramming and data via network communications.

Hence, aspects of the methods of the blood pressure monitoring and/orother processes, as outlined above, may be embodied in programming.Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine readable medium. Tangible non-transitory “storage” type mediainclude any or all of the memory or other storage for the computers,processors, or the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide storage at any time for the software programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer of the engine 200 into the hardwareplatform(s) of a computing environment or other system implementing acomputing environment or similar functionalities in connection with theblood pressure monitoring. Thus, another type of media that may bear thesoftware elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links or the like, also may be considered as mediabearing the software. As used herein, unless restricted to tangible“storage” media, terms such as computer or machine “readable medium”refer to any medium that participates in providing instructions to aprocessor for execution.

Hence, a machine-readable medium may take many forms, including atangible storage medium, a carrier wave medium or physical transmissionmedium. Non-volatile storage media include, for example, optical ormagnetic disks, such as any of the storage devices in any computer(s) orthe like, which may be used to implement the system or any of itscomponents as shown in the drawings. Volatile storage media includedynamic memory, such as a main memory of such a computer platform.Tangible transmission media include coaxial cables; copper wire andfiber optics, including the wires that form a bus within a computersystem. Carrier-wave transmission media may take the form of electric orelectromagnetic signals, or acoustic or light waves such as thosegenerated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present disclosure areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution—for example, an installation on an existing server. Inaddition, the blood pressure monitoring system as disclosed herein maybe implemented as a firmware, firmware/software combination,firmware/hardware combination, or a hardware/firmware/softwarecombination.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure. Inaddition, the term “logic” is representative of hardware, firmware,software (or any combination thereof) to perform one or more functions.For instance, examples of “hardware” include, but are not limited to, anintegrated circuit, a finite state machine, or even combinatorial logic.The integrated circuit may take the form of a processor such as amicroprocessor, an application specific integrated circuit, a digitalsignal processor, a micro-controller, or the like.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “circuit,” “unit,” “module,” “component,” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readable mediahaving computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or anysuitable combination thereof. A computer readable signal medium may beany computer readable medium that is not a computer readable storagemedium and that may communicate, propagate, or transport a program foruse by or in connection with an instruction execution system, apparatus,or device. Program code embodied on a computer readable signal mediummay be transmitted using any appropriate medium, including wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—for example, aninstallation on an existing server or mobile device. In addition, thefinancial management system disclosed herein may be implemented as afirmware, firmware/software combination, firmware/hardware combination,or a hardware/firmware/software combination.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

We claim:
 1. A device for monitoring blood pressure comprising: memorystoring instructions; and at least one processor that executes theinstructions to perform operations comprising: receiving a first signalrepresenting a pulse wave relating to heart activity of a subject;receiving a plurality of second signals representing time-varyinginformation on the pulse wave of the subject; obtaining a plurality ofmodels and a plurality of corresponding cost functions; extracting aplurality of effective physiological features corresponding to each ofthe plurality of models based on the first signal and the plurality ofsecond signals; designating a personalized model for the subject fromthe plurality of models based on the plurality of cost functions and theplurality of effective physiological features; and calculating a bloodpressure of the subject based on the effective physiological featuresand the designated personalized model for the subject.
 2. The device ofclaim 1, wherein the receiving the first signal comprises communicatingwith a first sensor configured to acquire the first signal of thesubject.
 3. The device of claim 1, wherein the receiving the pluralityof second signals comprises communicating with one or more secondsensors.
 4. The device of claim 1, wherein the effective physiologicalfeatures are obtained based on Akaike information criterion (AIC). 5.The device of claim 1, wherein the first signal or the second signalcomprises an ECG waveform, a PPG waveform, or a BCG waveform.
 6. Thedevice of claim 1 further being configured to communicate with acuff-based blood pressure monitor.
 7. The device of claim 6, thecuff-based blood pressure monitor being configured to coordinate a bloodpressure measurement with the receiving of the first signal or thereceiving of the plurality of second signals.
 8. A method implemented ona computing device having at least one processor and a non-transitorystorage medium for monitoring blood pressure, the method comprising:receiving a first signal representing a pulse wave relating to heartactivity of a subject; receiving a plurality of second signalsrepresenting time-varying information on the pulse wave of the subject;obtaining a plurality of models and a plurality of corresponding costfunctions; extracting a plurality of effective physiological featurescorresponding to each of the plurality of models based on the firstsignal and the plurality of second signals; designating a personalizedmodel for the subject from the plurality of models based on theplurality of cost functions and the plurality of effective physiologicalfeatures; and calculating a blood pressure of the subject based on theeffective physiological features and the designated personalized modelfor the subject.
 9. The method of claim 8, further comprising acquiringthe first signal at a first location on the body of the subject.
 10. Themethod of claim 8, further comprising acquiring the second signal at asecond location on the body of the subject.
 11. The method of claim 8,wherein the effective physiological features are obtained based onAkaike information criterion (AIC).
 12. The method of claim 8, whereinthe first signal or the second signal is acquired in real time or at afirst time interval.
 13. The method of claim 8, wherein a set ofcalibration data is acquired at a second time interval.
 14. A system formonitoring blood pressure, comprising at least one processor; anon-transitory storage medium; a first acquisition module configured toreceive a first signal representing heart activity of a subject; asecond acquisition module configured to receive a plurality of secondsignals representing time-varying information on a pulse wave of thesubject; a calibration unit configured to acquire a set of calibrationdata; an analysis module configured to obtaining a plurality of modelsand a plurality of corresponding cost functions; extracting a pluralityof effective physiological features corresponding to each of theplurality of models based on the first signal and the plurality ofsecond signals; designate a personalized model for the subject from theplurality of models based on the plurality of cost functions and theplurality of effective physiological features; and calculate a bloodpressure of the subject based on the effective physiological featuresand the designated personalized model for the subject.
 15. The system ofclaim 14, wherein the first acquisition module comprises an ECG monitor.16. The system of claim 14, wherein the second acquisition modulecomprises a blood oxygen monitor.
 17. The system of claim 14, whereinthe first signal or one of the plurality of second signals comprises anoptical signal or an electric signal.
 18. The system of claim 14,wherein the calibration unit is further configured to communicate with acuff-based blood pressure monitor.
 19. The system of claim 18, whereinthe cuff-based blood pressure monitor is further configured tocoordinate a blood pressure measurement with the first signal or theplurality of second signals.
 20. The system of claim 14, furthercomprising an output module configured to provide the calculated bloodpressure for output.